def compile_session_params_from_cfg(self, *args):
(model_params,) = args
self._params = TrainSessionParameters(
log = self._log,
mainOutputAbsFolder = self._main_out_folder_abs,
folderForSessionCnnModels = self._out_folder_models,
folderForPredictionsVal = self._out_folder_preds,
folderForFeaturesVal = self._out_folder_fms,
num_classes = model_params.numberClasses,
model_name = model_params.cnnModelName,
cfg = self._cfg )
self._log.print3("")
self._log.print3("============= NEW TRAINING SESSION ==============\n")
self._params.print_params()
self._log.print3("=======================================================\n")
return self._params
def compile_session_params_from_cfg(self, *args):
(model_params, ) = args
self._params = TrainSessionParameters(self._log,
self._main_out_folder_abs,
self._out_folder_models,
self._out_folder_preds,
self._out_folder_fms,
model_params.get_n_classes(),
model_params.get_model_name(),
self._cfg)
self._log.print3("")
self._log.print3(
"============= NEW TRAINING SESSION ==============\n")
self._params.print_params()
self._log.print3(
"=======================================================\n")
return self._params
def compile_session_params_from_cfg(self, *args):
(model_params,) = args
self._params = TrainSessionParameters(
log = self._log,
mainOutputAbsFolder = self._main_out_folder_abs,
folderForSessionCnnModels = self._out_folder_models,
folderForPredictionsVal = self._out_folder_preds,
folderForFeaturesVal = self._out_folder_fms,
num_classes = model_params.numberClasses,
model_name = model_params.cnnModelName,
cfg = self._cfg )
self._log.print3("")
self._log.print3("============= NEW TRAINING SESSION ==============\n")
self._params.print_params()
self._log.print3("=======================================================\n")
return self._params
def _make_sess_name(self):
sess_name = TrainSessionParameters.getSessionName(
self._cfg[self._cfg.SESSION_NAME])
return sess_name
class TrainSession(Session):
def __init__(self, cfg):
self._out_folder_models = None
self._out_folder_preds = None
self._out_folder_fms = None
self._params = None # Compiled from cfg. Required for run()
Session.__init__(self, cfg)
def _make_sess_name(self):
sess_name = TrainSessionParameters.getSessionName(
self._cfg[self._cfg.SESSION_NAME])
return sess_name
def make_output_folders(self):
self._main_out_folder_abs = getAbsPathEvenIfRelativeIsGiven(
self._cfg[self._cfg.FOLDER_OUTP], self.get_abs_path_to_cfg())
[
self._log_folder_abs, self._out_folder_models,
self._out_folder_preds, self._out_folder_fms
] = makeFoldersNeededForTrainingSession(self._main_out_folder_abs,
self._sess_name)
def _print_vars_in_collection(self, collection, coll_name="no_name"):
self._log.print3("")
self._log.print3("==== Printing variables of collection [" +
str(coll_name) + "] ====")
for entry in collection:
self._log.print3(str(entry))
self._log.print3("==== Done printing variables of collection. ====\n")
def compile_session_params_from_cfg(self, *args):
(model_params, ) = args
self._params = TrainSessionParameters(
log=self._log,
mainOutputAbsFolder=self._main_out_folder_abs,
folderForSessionCnnModels=self._out_folder_models,
folderForPredictionsVal=self._out_folder_preds,
folderForFeaturesVal=self._out_folder_fms,
num_classes=model_params.numberClasses,
model_name=model_params.cnnModelName,
cfg=self._cfg)
self._log.print3("")
self._log.print3(
"============= NEW TRAINING SESSION ==============\n")
self._params.print_params()
self._log.print3(
"=======================================================\n")
return self._params
def run_session(self, *args):
(sess_device, model_params, reset_trainer) = args
graphTf = tf.Graph()
with graphTf.as_default():
with graphTf.device(
sess_device
): # Explicit device assignment, throws an error if GPU is specified but not available.
self._log.print3(
"=========== Making the CNN graph... ===============")
cnn3d = Cnn3d()
with tf.variable_scope("net"):
cnn3d.make_cnn_model(*model_params.get_args_for_arch())
# I have now created the CNN graph. But not yet the Optimizer's graph.
# No explicit device assignment for the rest. Because trained has piecewise_constant that is only on cpu, and so is saver.
with tf.variable_scope("trainer"):
self._log.print3("=========== Building Trainer ===========\n")
trainer = Trainer(*(self._params.get_args_for_trainer() +
[cnn3d]))
trainer.create_optimizer(*self._params.get_args_for_optimizer(
)) # Trainer and net connect here.
# The below should not create any new tf.variables.
self._log.print3(
"=========== Compiling the Training Function ===========")
self._log.print3(
"=======================================================\n")
cnn3d.setup_ops_n_feeds_to_train(
self._log,
trainer.get_total_cost(),
trainer.get_param_updates_wrt_total_cost() # list of ops
)
self._log.print3(
"=========== Compiling the Validation Function =========")
cnn3d.setup_ops_n_feeds_to_val(self._log)
self._log.print3(
"=========== Compiling the Testing Function ============")
cnn3d.setup_ops_n_feeds_to_test(
self._log,
self._params.indices_fms_per_pathtype_per_layer_to_save
) # For validation with full segmentation
# Create the savers
saver_all = tf.train.Saver(
) # Will be used during training for saving everything.
collection_vars_net = tf.get_collection(
tf.GraphKeys.GLOBAL_VARIABLES, scope="net"
) # Alternative: tf.train.Saver([v for v in tf.all_variables() if v.name.startswith("net"])
saver_net = tf.train.Saver(var_list=collection_vars_net
) # Used to load the net's parameters.
collection_vars_trainer = tf.get_collection(
tf.GraphKeys.GLOBAL_VARIABLES, scope="trainer")
saver_trainer = tf.train.Saver(
var_list=collection_vars_trainer
) # Used to load the trainer's parameters.
# self._print_vars_in_collection(collection_vars_net, "net")
# self._print_vars_in_collection(collection_vars_trainer, "trainer")
with tf.Session(graph=graphTf,
config=tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False,
device_count={
'CPU': 999,
'GPU': 99
})) as sessionTf:
# with tf.Session( graph=graphTf, config=tf.ConfigProto(log_device_placement=False, device_count={'CPU':999, 'GPU':99})
# Load or initialize parameters
file_to_load_params_from = self._params.get_path_to_load_model_from(
)
if file_to_load_params_from is not None: # Load params
self._log.print3(
"=========== Loading parameters from specified saved model ==============="
)
chkpt_fname = tf.train.latest_checkpoint(
file_to_load_params_from) if os.path.isdir(
file_to_load_params_from) else file_to_load_params_from
self._log.print3("Loading checkpoint file:" + str(chkpt_fname))
self._log.print3("Loading network parameters...")
try:
saver_net.restore(sessionTf, chkpt_fname)
self._log.print3("Network parameters were loaded.")
except Exception as e:
handle_exception_tf_restore(self._log, e)
if not reset_trainer:
self._log.print3("Loading trainer parameters...")
saver_trainer.restore(sessionTf, chkpt_fname)
self._log.print3("Trainer parameters were loaded.")
else:
self._log.print3(
"Reset of trainer parameters was requested. Re-initializing them..."
)
tf.variables_initializer(
var_list=collection_vars_trainer).run()
self._log.print3("Trainer parameters re-initialized.")
else:
self._log.print3(
"=========== Initializing network and trainer variables ==============="
)
# tf.variables_initializer(var_list = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES) ).run() # Initializes all.
# Initialize separate as below, so that in case I miss a variable, I will get an error and I will know.
tf.variables_initializer(var_list=collection_vars_net).run()
tf.variables_initializer(
var_list=collection_vars_trainer).run()
self._log.print3("All variables were initialized.")
filename_to_save_with = self._params.filepath_to_save_models + ".initial." + datetimeNowAsStr(
)
self._log.print3("Saving the initial model at:" +
str(filename_to_save_with))
saver_all.save(sessionTf,
filename_to_save_with + ".model.ckpt",
write_meta_graph=False)
# tf.train.write_graph( graph_or_graph_def=sessionTf.graph.as_graph_def(), logdir="", name=filename_to_save_with+".graph.pb", as_text=False)
self._log.print3("")
self._log.print3(
"=======================================================")
self._log.print3(
"============== Training the CNN model =================")
self._log.print3(
"=======================================================\n")
res_code = do_training(
*([sessionTf, saver_all, cnn3d, trainer] +
self._params.get_args_for_train_routine()))
self._log.print3(
"\n=======================================================")
self._log.print3(
"=========== Training session finished =================")
self._log.print3(
"=======================================================")
class TrainSession(Session):
def __init__(self, cfg):
self._out_folder_models = None
self._out_folder_preds = None
self._out_folder_fms = None
self._out_folder_tensorboard = None
self._params = None # Compiled from cfg. Required for run()
Session.__init__(self, cfg)
def _make_sess_name(self):
sess_name = TrainSessionParameters.getSessionName(
self._cfg[self._cfg.SESSION_NAME])
return sess_name
def make_output_folders(self):
self._main_out_folder_abs = getAbsPathEvenIfRelativeIsGiven(
self._cfg[self._cfg.FOLDER_OUTP], self.get_abs_path_to_cfg())
[
self._log_folder_abs, self._out_folder_models,
self._out_folder_preds, self._out_folder_fms,
self._out_folder_tensorboard
] = makeFoldersNeededForTrainingSession(self._main_out_folder_abs,
self._sess_name)
def create_tensorboard_loggers(self,
logger_types,
tf_graph,
create_log=False):
tensorboard_loggers = {}
self._log.print3(
"----------- Creating Tensorboard Loggers -----------")
if create_log:
for logger_type in logger_types:
tb_log_path = os.path.join(self._out_folder_tensorboard,
logger_type)
if not os.path.exists(tb_log_path):
os.mkdir(tb_log_path
) # Separate folders for train / val metrics.
tensorboard_loggers[logger_type] = TensorboardLogger(
tb_log_path, tf_graph)
self._log.print3("Loggers created successfully")
else:
for logger_type in logger_types:
tensorboard_loggers[logger_type] = None
self._log.print3("Config flag to log to tensorboard not present.")
self._log.print3("Skipping...")
self._log.print3("-----------=============================-----------")
return tensorboard_loggers
def print_vars_in_collection(self, collection, coll_name="no_name"):
self._log.print3("")
self._log.print3("==== Printing variables of collection [" +
str(coll_name) + "] ====")
for entry in collection:
self._log.print3(str(entry))
self._log.print3("==== Done printing variables of collection. ====\n")
def compile_session_params_from_cfg(self, *args):
(model_params, ) = args
self._params = TrainSessionParameters(
log=self._log,
mainOutputAbsFolder=self._main_out_folder_abs,
folderForSessionCnnModels=self._out_folder_models,
folderForPredictionsVal=self._out_folder_preds,
folderForFeaturesVal=self._out_folder_fms,
num_classes=model_params.numberClasses,
model_name=model_params.cnnModelName,
cfg=self._cfg)
self._log.print3("")
self._log.print3(
"============= NEW TRAINING SESSION ==============\n")
self._params.print_params()
self._log.print3(
"=======================================================\n")
return self._params
def run_session(self, *args):
(sess_device, model_params, reset_trainer) = args
graphTf = tf.Graph()
with graphTf.as_default():
# Explicit device assignment, throws an error if GPU is specified but not available.
with tf.device(sess_device):
self._log.print3(
"=========== Making the CNN graph... ===============")
cnn3d = Cnn3d()
with tf.compat.v1.variable_scope("net"):
cnn3d.make_cnn_model(*model_params.get_args_for_arch())
# I have now created the CNN graph. But not yet the Optimizer's graph.
inp_plchldrs_train, inp_shapes_per_path_train = cnn3d.create_inp_plchldrs(
model_params.get_inp_dims_hr_path('train'), 'train')
inp_plchldrs_val, inp_shapes_per_path_val = cnn3d.create_inp_plchldrs(
model_params.get_inp_dims_hr_path('val'), 'val')
inp_plchldrs_test, inp_shapes_per_path_test = cnn3d.create_inp_plchldrs(
model_params.get_inp_dims_hr_path('test'), 'test')
p_y_given_x_train = cnn3d.apply(inp_plchldrs_train,
'train',
'train',
verbose=True,
log=self._log)
p_y_given_x_val = cnn3d.apply(inp_plchldrs_val,
'infer',
'val',
verbose=True,
log=self._log)
p_y_given_x_test = cnn3d.apply(inp_plchldrs_test,
'infer',
'test',
verbose=True,
log=self._log)
# No explicit device assignment for the rest.
# Because trained has piecewise_constant that is only on cpu, and so is saver.
with tf.compat.v1.variable_scope("trainer"):
self._log.print3("=========== Building Trainer ===========\n")
trainer = Trainer(*(self._params.get_args_for_trainer() +
[cnn3d]))
trainer.compute_costs(self._log, p_y_given_x_train)
trainer.create_optimizer(*self._params.get_args_for_optimizer(
)) # Trainer and net connect here.
tensorboard_loggers = self.create_tensorboard_loggers(
['train', 'val'],
graphTf,
create_log=self._params.get_tensorboard_bool())
# The below should not create any new tf.variables.
self._log.print3(
"=========== Compiling the Training Function ===========")
self._log.print3(
"=======================================================\n")
cnn3d.setup_ops_n_feeds_to_train(
self._log,
inp_plchldrs_train,
p_y_given_x_train,
trainer.get_total_cost(),
trainer.get_param_updates_wrt_total_cost() # list of ops
)
self._log.print3(
"=========== Compiling the Validation Function =========")
cnn3d.setup_ops_n_feeds_to_val(self._log, inp_plchldrs_val,
p_y_given_x_val)
self._log.print3(
"=========== Compiling the Testing Function ============")
# For validation with full segmentation
cnn3d.setup_ops_n_feeds_to_test(
self._log, inp_plchldrs_test, p_y_given_x_test,
self._params.indices_fms_per_pathtype_per_layer_to_save)
# Create the savers
saver_all = tf.compat.v1.train.Saver(
) # Will be used during training for saving everything.
# Alternative: tf.train.Saver([v for v in tf.all_variables() if v.name.startswith("net"])
collection_vars_net = tf.compat.v1.get_collection(
tf.compat.v1.GraphKeys.GLOBAL_VARIABLES, scope="net")
saver_net = tf.compat.v1.train.Saver(
var_list=collection_vars_net
) # Used to load the net's parameters.
collection_vars_trainer = tf.compat.v1.get_collection(
tf.compat.v1.GraphKeys.GLOBAL_VARIABLES, scope="trainer")
saver_trainer = tf.compat.v1.train.Saver(
var_list=collection_vars_trainer
) # Used to load the trainer's parameters.
# TF2: dict_vars_net = {'net_var'+str(i): v for i, v in enumerate(collection_vars_net)}
# TF2: dict_vars_trainer = {'trainer_var'+str(i): v for i, v in enumerate(collection_vars_trainer)}
# TF2: dict_vars_all = dict_vars_net.copy()
# TF2: for key in dict_vars_trainer:
# TF2: dict_vars_all[key] = dict_vars_trainer[key]
# TF2: ckpt_all = tf.train.Checkpoint(**dict_vars_all)
# TF2: ckpt_net = tf.train.Checkpoint(**dict_vars_net)
# TF2: ckpt_trainer = tf.train.Checkpoint(**dict_vars_trainer)
# self._print_vars_in_collection(collection_vars_net, "net")
# self._print_vars_in_collection(collection_vars_trainer, "trainer")
with tf.compat.v1.Session(graph=graphTf,
config=tf.compat.v1.ConfigProto(
log_device_placement=False,
device_count={
'CPU': 999,
'GPU': 99
})) as sessionTf:
# Load or initialize parameters
file_to_load_params_from = self._params.get_path_to_load_model_from(
)
if file_to_load_params_from is not None: # Load params
self._log.print3(
"=========== Loading parameters from specified saved model ==============="
)
chkpt_fname = tf.train.latest_checkpoint(file_to_load_params_from) \
if os.path.isdir(file_to_load_params_from) else file_to_load_params_from
self._log.print3("Loading checkpoint file:" + str(chkpt_fname))
self._log.print3("Loading network parameters...")
try:
saver_net.restore(sessionTf, chkpt_fname)
# TF2: status = ckpt_net.restore(chkpt_fname); #status.assert_consumed() # Passes if ckpt and program vars match exactly.
self._log.print3("Network parameters were loaded.")
except Exception as e:
handle_exception_tf_restore(self._log, e)
if not reset_trainer:
self._log.print3("Loading trainer parameters...")
saver_trainer.restore(sessionTf, chkpt_fname)
# TF2: status = ckpt_trainer.restore(chkpt_fname); #status.assert_consumed() # Passes if ckpt and program vars match exactly.
self._log.print3("Trainer parameters were loaded.")
else:
self._log.print3(
"Reset of trainer parameters was requested. Re-initializing them..."
)
tf.compat.v1.variables_initializer(
var_list=collection_vars_trainer).run()
self._log.print3("Trainer parameters re-initialized.")
else:
self._log.print3(
"=========== Initializing network and trainer variables ==============="
)
# Initializes all.
# tf.variables_initializer(var_list = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES) ).run()
# Initialize separate as below, so that in case I miss a variable, I will get an error and I will know.
tf.compat.v1.variables_initializer(
var_list=collection_vars_net).run()
tf.compat.v1.variables_initializer(
var_list=collection_vars_trainer).run()
self._log.print3("All variables were initialized.")
filename_to_save_with = self._params.filepath_to_save_models + ".initial." + datetime_now_str(
)
self._log.print3("Saving the initial model at:" +
str(filename_to_save_with))
saver_all.save(sessionTf,
filename_to_save_with + ".model.ckpt",
write_meta_graph=False)
# TF2: ckpt_all.save(file_prefix = filename_to_save_with+".all.ckpt2")
# TF2: ckpt_net.save(file_prefix = filename_to_save_with+".net.ckpt2")
# TF2: ckpt_trainer.save(file_prefix = filename_to_save_with+".trainer.ckpt2")
# tf.train.write_graph(graph_or_graph_def=sessionTf.graph.as_graph_def(),
# logdir="", name=filename_to_save_with+".graph.pb", as_text=False)
self._log.print3("")
self._log.print3(
"=======================================================")
self._log.print3(
"============== Training the CNN model =================")
self._log.print3(
"=======================================================")
do_training(*([sessionTf, saver_all, cnn3d, trainer, tensorboard_loggers] +\
self._params.get_args_for_train_routine() +\
[inp_shapes_per_path_train, inp_shapes_per_path_val, inp_shapes_per_path_test]))
# TF2: ckpt_all.save(file_prefix = filename_to_save_with+".all.FINAL.ckpt2")
# TF2: ckpt_net.save(file_prefix = filename_to_save_with+".net.FINAL.ckpt2")
# TF2: ckpt_trainer.save(file_prefix = filename_to_save_with+".trainer.FINAL.ckpt2")
self._log.print3(
"\n=======================================================")
self._log.print3(
"=========== Training session finished =================")
self._log.print3(
"=======================================================")
def _make_sess_name(self):
sess_name = TrainSessionParameters.getSessionName( self._cfg[self._cfg.SESSION_NAME] )
return sess_name
class TrainSession(Session):
def __init__(self, cfg):
self._out_folder_models = None
self._out_folder_preds = None
self._out_folder_fms = None
self._params = None # Compiled from cfg. Required for run()
Session.__init__(self, cfg)
def _make_sess_name(self):
sess_name = TrainSessionParameters.getSessionName( self._cfg[self._cfg.SESSION_NAME] )
return sess_name
def make_output_folders(self):
self._main_out_folder_abs = getAbsPathEvenIfRelativeIsGiven( self._cfg[self._cfg.FOLDER_OUTP],
self.get_abs_path_to_cfg() )
[self._log_folder_abs,
self._out_folder_models,
self._out_folder_preds,
self._out_folder_fms] = makeFoldersNeededForTrainingSession( self._main_out_folder_abs, self._sess_name )
def _print_vars_in_collection(self, collection, coll_name="no_name"):
self._log.print3("")
self._log.print3("==== Printing variables of collection [" +str(coll_name) + "] ====")
for entry in collection:
self._log.print3(str(entry))
self._log.print3("==== Done printing variables of collection. ====\n")
def compile_session_params_from_cfg(self, *args):
(model_params,) = args
self._params = TrainSessionParameters(
log = self._log,
mainOutputAbsFolder = self._main_out_folder_abs,
folderForSessionCnnModels = self._out_folder_models,
folderForPredictionsVal = self._out_folder_preds,
folderForFeaturesVal = self._out_folder_fms,
num_classes = model_params.numberClasses,
model_name = model_params.cnnModelName,
cfg = self._cfg )
self._log.print3("")
self._log.print3("============= NEW TRAINING SESSION ==============\n")
self._params.print_params()
self._log.print3("=======================================================\n")
return self._params
def run_session(self, *args):
(sess_device,
model_params,
reset_trainer) = args
graphTf = tf.Graph()
with graphTf.as_default():
with graphTf.device(sess_device): # Explicit device assignment, throws an error if GPU is specified but not available.
self._log.print3("=========== Making the CNN graph... ===============")
cnn3d = Cnn3d()
with tf.variable_scope("net"):
cnn3d.make_cnn_model( *model_params.get_args_for_arch() )
# I have now created the CNN graph. But not yet the Optimizer's graph.
# No explicit device assignment for the rest. Because trained has piecewise_constant that is only on cpu, and so is saver.
with tf.variable_scope("trainer"):
self._log.print3("=========== Building Trainer ===========\n")
trainer = Trainer( *( self._params.get_args_for_trainer() + [cnn3d] ) )
trainer.create_optimizer( *self._params.get_args_for_optimizer() ) # Trainer and net connect here.
# The below should not create any new tf.variables.
self._log.print3("=========== Compiling the Training Function ===========")
self._log.print3("=======================================================\n")
cnn3d.setup_ops_n_feeds_to_train( self._log,
trainer.get_total_cost(),
trainer.get_param_updates_wrt_total_cost() # list of ops
)
self._log.print3("=========== Compiling the Validation Function =========")
cnn3d.setup_ops_n_feeds_to_val( self._log )
self._log.print3("=========== Compiling the Testing Function ============")
cnn3d.setup_ops_n_feeds_to_test( self._log,
self._params.indices_fms_per_pathtype_per_layer_to_save ) # For validation with full segmentation
# Create the savers
saver_all = tf.train.Saver() # Will be used during training for saving everything.
collection_vars_net = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope="net") # Alternative: tf.train.Saver([v for v in tf.all_variables() if v.name.startswith("net"])
saver_net = tf.train.Saver( var_list = collection_vars_net ) # Used to load the net's parameters.
collection_vars_trainer = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope="trainer")
saver_trainer = tf.train.Saver( var_list = collection_vars_trainer ) # Used to load the trainer's parameters.
# self._print_vars_in_collection(collection_vars_net, "net")
# self._print_vars_in_collection(collection_vars_trainer, "trainer")
with tf.Session( graph=graphTf, config=tf.ConfigProto(log_device_placement=False, device_count={'CPU':999, 'GPU':99}) ) as sessionTf:
# Load or initialize parameters
file_to_load_params_from = self._params.get_path_to_load_model_from()
if file_to_load_params_from is not None: # Load params
self._log.print3("=========== Loading parameters from specified saved model ===============")
chkpt_fname = tf.train.latest_checkpoint( file_to_load_params_from ) if os.path.isdir( file_to_load_params_from ) else file_to_load_params_from
self._log.print3("Loading checkpoint file:" + str(chkpt_fname))
self._log.print3("Loading network parameters...")
try:
saver_net.restore(sessionTf, chkpt_fname)
self._log.print3("Network parameters were loaded.")
except Exception as e: handle_exception_tf_restore(self._log, e)
if not reset_trainer:
self._log.print3("Loading trainer parameters...")
saver_trainer.restore(sessionTf, chkpt_fname)
self._log.print3("Trainer parameters were loaded.")
else:
self._log.print3("Reset of trainer parameters was requested. Re-initializing them...")
tf.variables_initializer(var_list = collection_vars_trainer).run()
self._log.print3("Trainer parameters re-initialized.")
else :
self._log.print3("=========== Initializing network and trainer variables ===============")
# tf.variables_initializer(var_list = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES) ).run() # Initializes all.
# Initialize separate as below, so that in case I miss a variable, I will get an error and I will know.
tf.variables_initializer(var_list = collection_vars_net).run()
tf.variables_initializer(var_list = collection_vars_trainer).run()
self._log.print3("All variables were initialized.")
filename_to_save_with = self._params.filepath_to_save_models + ".initial." + datetimeNowAsStr()
self._log.print3("Saving the initial model at:" + str(filename_to_save_with))
saver_all.save( sessionTf, filename_to_save_with+".model.ckpt", write_meta_graph=False )
# tf.train.write_graph( graph_or_graph_def=sessionTf.graph.as_graph_def(), logdir="", name=filename_to_save_with+".graph.pb", as_text=False)
self._log.print3("")
self._log.print3("=======================================================")
self._log.print3("============== Training the CNN model =================")
self._log.print3("=======================================================\n")
res_code = do_training( *( [sessionTf, saver_all, cnn3d, trainer] + self._params.get_args_for_train_routine() ) )
self._log.print3("\n=======================================================")
self._log.print3("=========== Training session finished =================")
self._log.print3("=======================================================")
