def change_model(config: Configuration, start_time_string):
search_dir = config.models_folder
loss_to_dir = {}
for subdir, dirs, files in os.walk(search_dir):
for directory in dirs:
# Only "temporary models" are created by the optimizer, other shouldn't be considered
if not directory.startswith('temp_snn_model'):
continue
# The model must have been created after the training has began, older ones are from other training runs
date_string_model = '_'.join(directory.split('_')[3:5])
date_model = datetime.strptime(date_string_model, "%m-%d_%H-%M-%S")
date_start = datetime.strptime(start_time_string, "%m-%d_%H-%M-%S")
if date_start > date_model:
continue
# Read the loss for the current model from the loss.txt file and add to dictionary
path_loss_file = os.path.join(search_dir, directory, 'loss.txt')
if os.path.isfile(path_loss_file):
with open(path_loss_file) as f:
try:
loss = float(f.readline())
except ValueError:
print('Could not read loss from loss.txt for',
directory)
continue
if loss not in loss_to_dir.keys():
loss_to_dir[loss] = directory
else:
print('Could not read loss from loss.txt for', directory)
# Select the best loss and change the config to the corresponding model
min_loss = min(list(loss_to_dir.keys()))
config.filename_model_to_use = loss_to_dir.get(min_loss)
config.directory_model_to_use = config.models_folder + config.filename_model_to_use + '/'
print('Model selected for inference:')
print(config.directory_model_to_use, '\n')
def change_model(config: Configuration, start_time_string, num_of_selction_iteration = None, get_model_by_loss_value = None):
search_dir = config.models_folder
loss_to_dir = {}
for subdir, dirs, files in os.walk(search_dir):
for directory in dirs:
# Only "temporary models" are created by the optimizer, other shouldn't be considered
if not directory.startswith('temp_snn_model'):
continue
# The model must have been created after the training has began, older ones are from other training runs
date_string_model = '_'.join(directory.split('_')[3:5])
date_model = datetime.strptime(date_string_model, "%m-%d_%H-%M-%S")
date_start = datetime.strptime(start_time_string, "%m-%d_%H-%M-%S")
if date_start > date_model:
continue
# Read the loss for the current model from the loss.txt file and add to dictionary
path_loss_file = os.path.join(search_dir, directory, 'loss.txt')
if os.path.isfile(path_loss_file):
with open(path_loss_file) as f:
try:
loss = float(f.readline())
except ValueError:
print('Could not read loss from loss.txt for', directory)
continue
if loss not in loss_to_dir.keys():
loss_to_dir[loss] = directory
else:
print('Could not read loss from loss.txt for', directory)
if num_of_selction_iteration == None and get_model_by_loss_value == None:
# Select the best loss and change the config to the corresponding model
min_loss = min(list(loss_to_dir.keys()))
config.filename_model_to_use = loss_to_dir.get(min_loss)
config.directory_model_to_use = config.models_folder + config.filename_model_to_use + '/'
print('Model selected for inference:')
print(config.directory_model_to_use, '\n')
elif num_of_selction_iteration is not None and get_model_by_loss_value == None:
# Select k-th (num_of_selction_iteration) best loss and change the config to the corresponding model
loss_list = (list(loss_to_dir.keys()))
loss_list.sort()
min_loss = min(list(loss_to_dir.keys()))
selected_loss = loss_list[num_of_selction_iteration]
config.filename_model_to_use = loss_to_dir.get(selected_loss)
config.directory_model_to_use = config.models_folder + config.filename_model_to_use + '/'
print("Selection: ", num_of_selction_iteration, ' for model with loss: ', selected_loss, "(min loss:", min_loss,")", 'selected for evaluation on the validation set:')
print(config.directory_model_to_use, '\n')
return selected_loss
elif get_model_by_loss_value is not None:
# Select a model by a given loss value (as key) and change the config to the corresponding model
config.filename_model_to_use = loss_to_dir.get(get_model_by_loss_value)
config.directory_model_to_use = config.models_folder + config.filename_model_to_use + '/'
print('Model selected for inference by a given key (loss):')
print(config.directory_model_to_use, '\n')
