def train(args):
filepath = "weights-{epoch:03d}-{val_loss:.4f}-{val_mean_iou:.4f}.h5"
weights_dir = os.path.join(args.weights, args.backBone + '_' + args.model)
cfg.check_folder(weights_dir)
model_weights = os.path.join(weights_dir, filepath)
# build the model
model, base_model = builder(cfg.n_classes, (256, 256), args.model, args.backBone)
model.summary()
# compile the model
#sgd = optimizers.SGD(lr=cfg.lr, momentum=0.9)
nadam = optimizers.Nadam(lr=cfg.lr, beta_1=0.9, beta_2=0.999, epsilon=1e-8)
model.compile(optimizer=nadam, loss='categorical_crossentropy', metrics=[MeanIoU(cfg.n_classes)])
# checkpoint setting
model_checkpoint = ModelCheckpoint(model_weights, monitor='val_loss', save_best_only=True, mode='auto')
# learning rate scheduler setting
lr_decay = lr_decays_func(args.lr_scheduler, args.learning_rate, args.num_epochs, args.lr_warmup)
learning_rate_scheduler = LearningRateScheduler(lr_decay, args.learning_rate, args.lr_warmup, cfg.steps_per_epoch,
num_epochs=args.num_epochs, verbose=1)
callbacks = [model_checkpoint]
# training...
train_set = dataloader.train_data_generator(cfg.train_data_path, cfg.train_label_path, cfg.batch_size,
cfg.n_classes, cfg.data_augment)
val_set = dataloader.val_data_generator(cfg.val_data_path, cfg.val_label_path, cfg.batch_size, cfg.n_classes)
start_epoch = 0
if os.path.exists(weights_dir) and os.listdir(weights_dir):
a = sorted(file for file in os.listdir(weights_dir))
model.load_weights(weights_dir + '/' + a[-1], by_name=True)
# if load success, output info
print('loaded :' + '-' * 8 + weights_dir + '/' + a[-1])
start_epoch = int(a[-1][8:11])
model.fit(train_set,
steps_per_epoch=cfg.steps_per_epoch,
epochs=args.num_epochs,
callbacks=callbacks,
validation_data=val_set,
validation_steps=cfg.validation_steps,
max_queue_size= cfg.batch_size,
initial_epoch=start_epoch)
default=True)
args = parser.parse_args()
# check related paths
paths = check_related_path(os.getcwd())
# check the image path
if not os.path.exists(args.image_path):
raise ValueError(
'The path \'{image_path}\' does not exist the image file.'.format(
image_path=args.image_path))
# build the model
net, base_model = builder(args.num_classes,
(args.crop_height, args.crop_width), args.model,
args.base_model)
# load weights
print('Loading the weights...')
if args.weights is None:
net.load_weights(filepath=os.path.join(
paths['weigths_path'], '{model}_based_on_{base_model}.h5'.format(
model=args.model, base_model=base_model)))
else:
if not os.path.exists(args.weights):
raise ValueError(
'The weights file does not exist in \'{path}\''.format(
path=args.weights))
net.load_weights(args.weights)
from builders import builder
from solvers import solves
from utilities import load_params, dump_result
if __name__ == '__main__':
params = load_params("params.json")
terms = builder(nmax=params['nmax'],
t_lower_bound=params['t_lower_bound'],
t_pivot=params['t_pivot'],
t_upper_bound=params['t_upper_bound'],
n1=params['n1'],
n2=params['n2'],
delta=params['delta'],
MU=params['MU'],
U=params['U'],
V=params['V'],
W=params['W'],
mu_lower_bound=params['mu_lower_bound'],
mu_upper_bound=params['mu_upper_bound'],
ma=params['ma'])
result = solves(hexagon_mf_operators=terms['hexagon_mf_operators'],
t_a=terms['t_a'],
t_b=terms['t_b'],
ts=terms['ts'],
Ma=terms['Ma'],
uab_term=terms['uab_term'],
u_term=terms['u_term'],
v_term=terms['v_term'],
mu_term=terms['mu_term'],
t_term=terms['t_term'],
var_terms=terms['var_terms'],
def train(args):
filepath = "weights-{epoch:03d}-{val_loss:.4f}-{val_mean_iou:.4f}.h5"
weights_dir = os.path.join(args.weights, args.backBone + '_' + args.model)
cfg.check_folder(weights_dir)
model_weights = os.path.join(weights_dir, filepath)
# build the model
model, base_model = builder(cfg.n_classes, (256, 256), args.model,
args.backBone)
model.summary()
# compile the model
sgd = optimizers.SGD(lr=0.0001, momentum=0.9)
adam = optimizers.Adam(lr=cfg.lr, beta_1=0.9, beta_2=0.999, epsilon=1e-8)
model.compile(optimizer=sgd,
loss='categorical_crossentropy',
metrics=[MeanIoU(cfg.n_classes)])
# checkpoint setting
model_checkpoint = ModelCheckpoint(model_weights,
monitor='val_loss',
save_best_only=True,
mode='auto')
# learning rate scheduler setting
lr_decay = lr_decays_func(args.lr_scheduler, args.learning_rate,
args.num_epochs, args.lr_warmup)
learning_rate_scheduler = LearningRateScheduler(lr_decay,
args.learning_rate,
args.lr_warmup,
cfg.steps_per_epoch,
num_epochs=args.num_epochs,
verbose=1)
Reduce_LR = ReduceLROnPlateau(monitor='val_mean_iou',
mode='max',
patience=2,
verbose=1,
factor=0.2,
min_lr=1e-7)
# callbacks = [model_checkpoint]
callbacks = [model_checkpoint, Reduce_LR]
# training...
train_set = dataloader.train_data_generator(cfg.train_data_path,
cfg.train_label_path,
cfg.batch_size, cfg.n_classes,
cfg.data_augment)
val_set = dataloader.val_data_generator(cfg.val_data_path,
cfg.val_label_path, cfg.batch_size,
cfg.n_classes)
start_epoch = 0
if os.path.exists(weights_dir) and os.listdir(weights_dir):
a = sorted(file for file in os.listdir(weights_dir))
model.load_weights(weights_dir + '/' + a[-1], by_name=True)
# if load success, output info
print('loaded :' + '-' * 8 + weights_dir + '/' + a[-1])
start_epoch = int(a[-1][8:11])
for layer in model.layers:
layer.trainable = False
for i in range(-1, -27, -1):
model.layers[i].trainable = True
model.summary()
print("start_epoch: ", start_epoch)
if start_epoch == 0:
backbone_pretrained_path = "backBone_pretrained_weights/" + 'densenet121_weights_tf_dim_ordering_tf_kernels_notop.h5'
model.load_weights(backbone_pretrained_path, by_name=True)
print(f"loaded : {backbone_pretrained_path}")
print(len(model.layers))
for layer in model.layers:
layer.trainable = False
for i in range(-1, -27, -1):
model.layers[i].trainable = True
model.summary()
model.fit(train_set,
steps_per_epoch=cfg.steps_per_epoch,
epochs=args.num_epochs,
callbacks=callbacks,
validation_data=val_set,
validation_steps=cfg.validation_steps,
max_queue_size=cfg.batch_size,
initial_epoch=start_epoch)
csv_file = 'CamVid/class_dict.csv'
num_classes = 32
crop_height = 256
crop_width = 256
weights = 'weights/{Weight-name}.h5'
image_path = 'predictions/{Image-name}.png'
color_encode1 = True
paths = check_related_path(os.getcwd())
if not os.path.exists(image_path):
raise ValueError(
'The path \'{image_path}\' does not exist the image file.'.format(
image_path=image_path))
net, base_model = builder(num_classes, (crop_height, crop_width), model,
base_model)
print('Loading the weights...')
if weights is None:
net.load_weights(filepath=os.path.join(
paths['weigths_path'], '{model}_based_on_{base_model}.h5'.format(
model=model, base_model=base_model)))
else:
if not os.path.exists(weights):
raise ValueError(
'The weights file does not exist in \'{path}\''.format(
path=weights))
net.load_weights(weights)
print("\n***** Begin testing *****")
print("Model -->", model)
