def train_from_config(config, project_folder):
#added for compatibility with < 0.5.7 versions
try:
input_size = config['model']['input_size'][:]
except:
input_size = [
config['model']['input_size'], config['model']['input_size']
]
# Create the converter
converter = Converter(config['converter']['type'],
config['model']['architecture'], input_size,
config['train']['valid_image_folder'])
# Detector
if config['model']['type'] == 'Detector':
if config['train']['is_only_detect']:
labels = ["object"]
else:
if config['model']['labels']:
labels = config['model']['labels']
else:
labels = get_object_labels(
config['train']['train_annot_folder'])
print("labels: ", labels)
print("training on: ", config['train']['train_image_folder'],
"and validating on: ", config['train']['train_annot_folder'])
# 1. Construct the model
yolo = create_yolo(
config['model']['architecture'], labels, input_size,
config['model']['anchors'], config['model']['coord_scale'],
config['model']['class_scale'], config['model']['object_scale'],
config['model']['no_object_scale'], config['weights']['backend'])
# 2. Load the pretrained weights (if any)
yolo.load_weights(config['weights']['full'], by_name=True)
# 3. actual training
model_layers, model_path = yolo.train(
config['train']['train_image_folder'],
config['train']['train_annot_folder'],
config['train']['actual_epoch'], project_folder,
config["train"]["batch_size"], config["train"]["augumentation"],
config['train']['learning_rate'], config['train']['train_times'],
config['train']['valid_times'],
config['train']['valid_image_folder'],
config['train']['valid_annot_folder'],
config['train']['first_trainable_layer'],
config['train']['valid_metric'])
# 4 Convert the model
converter.convert_model(model_path, model_layers,
config['train']['valid_image_folder'])
return model_path
def train_from_config(config, project_folder):
#added for compatibility with < 0.5.7 versions
try:
input_size = config['model']['input_size'][:]
except:
input_size = [
config['model']['input_size'], config['model']['input_size']
]
# Create the converter
converter = Converter(config['converter']['type'],
config['model']['architecture'], input_size,
config['train']['valid_image_folder'])
# Segmentation network
if config['model']['type'] == 'SegNet':
print('Segmentation')
# 1. Construct the model
segnet = create_segnet(config['model']['architecture'], input_size,
config['model']['n_classes'],
config['weights']['backend'])
# 2. Load the pretrained weights (if any)
segnet.load_weights(config['weights']['full'], by_name=True)
# 3. actual training
model_layers, model_path = segnet.train(
config['train']['train_image_folder'],
config['train']['train_annot_folder'],
config['train']['actual_epoch'], project_folder,
config["train"]["batch_size"], config["train"]["augumentation"],
config['train']['learning_rate'], config['train']['train_times'],
config['train']['valid_times'],
config['train']['valid_image_folder'],
config['train']['valid_annot_folder'],
config['train']['first_trainable_layer'],
config['train']['ignore_zero_class'],
config['train']['valid_metric'])
# Classifier
if config['model']['type'] == 'Classifier':
print('Classifier')
if config['model']['labels']:
labels = config['model']['labels']
else:
labels = get_labels(config['train']['train_image_folder'])
# 1. Construct the model
classifier = create_classifier(config['model']['architecture'], labels,
input_size,
config['model']['fully-connected'],
config['model']['dropout'],
config['weights']['backend'],
config['weights']['save_bottleneck'])
# 2. Load the pretrained weights (if any)
classifier.load_weights(config['weights']['full'], by_name=True)
# 3. actual training
model_layers, model_path = classifier.train(
config['train']['train_image_folder'],
config['train']['actual_epoch'], project_folder,
config["train"]["batch_size"], config["train"]["augumentation"],
config['train']['learning_rate'], config['train']['train_times'],
config['train']['valid_times'],
config['train']['valid_image_folder'],
config['train']['first_trainable_layer'],
config['train']['valid_metric'])
# Detector
if config['model']['type'] == 'Detector':
if config['train']['is_only_detect']:
labels = ["object"]
else:
if config['model']['labels']:
labels = config['model']['labels']
else:
labels = get_object_labels(
config['train']['train_annot_folder'])
print(labels)
# 1. Construct the model
yolo = create_yolo(
config['model']['architecture'], labels, input_size,
config['model']['anchors'], config['model']['coord_scale'],
config['model']['class_scale'], config['model']['object_scale'],
config['model']['no_object_scale'], config['weights']['backend'])
# 2. Load the pretrained weights (if any)
yolo.load_weights(config['weights']['full'], by_name=True)
# 3. actual training
model_layers, model_path = yolo.train(
config['train']['train_image_folder'],
config['train']['train_annot_folder'],
config['train']['actual_epoch'], project_folder,
config["train"]["batch_size"], config["train"]["augumentation"],
config['train']['learning_rate'], config['train']['train_times'],
config['train']['valid_times'],
config['train']['valid_image_folder'],
config['train']['valid_annot_folder'],
config['train']['first_trainable_layer'],
config['train']['valid_metric'])
# 4 Convert the model
converter.convert_model(model_path, model_layers,
config['train']['valid_image_folder'])
return model_path