def default_config():
train_data = None # Directory with training data
eval_data = None # Directory with validation data
model_output_dir = None # Directory to output tf model
restore_model = False # Set to true to continue training
classes_file = None # txt file with classes values (unused for REGRESSION)
gpu = '' # GPU to be used for training
prediction_type = utils.PredictionType.CLASSIFICATION # One of CLASSIFICATION, REGRESSION or MULTILABEL
pretrained_model_name = 'resnet50'
model_params = utils.ModelParams(pretrained_model_name=pretrained_model_name).to_dict() # Model parameters
training_params = utils.TrainingParams().to_dict() # Training parameters
if prediction_type == utils.PredictionType.CLASSIFICATION:
assert classes_file is not None
model_params['n_classes'] = utils.get_n_classes_from_file(classes_file)
elif prediction_type == utils.PredictionType.REGRESSION:
model_params['n_classes'] = 1
elif prediction_type == utils.PredictionType.MULTILABEL:
assert classes_file is not None
model_params['n_classes'] = utils.get_n_classes_from_file_multilabel(classes_file)
def default_config():
#train_data = "Saumweg_training_combined.csv" # Directory with training data
train_data = "/scratch/users/ycan/dhSegment/zistovi/train/"
eval_data = "/scratch/users/ycan/dhSegment/zistovi/train/" # Directory with validation data
model_output_dir = "model_zistovi_unet_all_100/" # Directory to output tf model
restore_model = False # Set to true to continue training
classes_file = "classes.txt" # txt file with classes values (unused for REGRESSION)
gpu = '0' # GPU to be used for training
prediction_type = utils.PredictionType.CLASSIFICATION # One of CLASSIFICATION, REGRESSION or MULTILABEL
pretrained_model_name = 'unet'
model_params = utils.ModelParams(pretrained_model_name=pretrained_model_name).to_dict() # Model parameters
training_params = utils.TrainingParams().to_dict() # Training parameters
if prediction_type == utils.PredictionType.CLASSIFICATION:
assert classes_file is not None
model_params['n_classes'] = utils.get_n_classes_from_file(classes_file)
elif prediction_type == utils.PredictionType.REGRESSION:
model_params['n_classes'] = 1
elif prediction_type == utils.PredictionType.MULTILABEL:
assert classes_file is not None
model_params['n_classes'] = utils.get_n_classes_from_file_multilabel(classes_file)
import os