def main(args):
# set the necessary list i.e. list of names of images to be read
test_list = pd.read_csv(args.test_list, header=None)
# set the necessary directories
testimg_dir = args.testimg_dir
testmsk_dir = args.testmsk_dir
# Generate batch data for SGD
# NOTE: This helps control the batch size for each test set
test_gen = data_gen_small(testimg_dir, testmsk_dir, test_list,
args.batch_size,
[args.input_shape[0], args.input_shape[1]],
args.n_labels)
# Create a model
if args.model == 'unet':
model = unet(\
args.input_shape, args.n_labels, args.kernel, \
args.pool_size, args.output_mode, args.gpus)
elif args.model == 'segnet':
model = segnet(\
args.input_shape, args.n_labels, args.kernel, \
args.pool_size, args.output_mode, args.gpus)
elif args.model == 'segunet':
model = segunet(\
args.input_shape, args.n_labels, args.kernel, \
args.pool_size, args.output_mode, args.gpus)
# TODO: Configure the model for training
optimizer = Adadelta(
lr=1.0, rho=0.95, epsilon=None, decay=0.01
) # NOTE: The details doesn't matter here because we are not training the model
model.compile(loss=args.loss, optimizer=optimizer, metrics=[args.metrics])
# Set model trained weights
model.load_weights(args.model_weights)
# Keras moodel summary, for confirmation
print(model.summary())
# Test the model on data generated batch-by-batch by a Python generator
# NOTE: We use evaluate_generator because we do provide our generated dataset with specific batch size
tensorboard = TensorBoard(log_dir=args.save_dir)
fit_start_time = time()
model.evaluate_generator(test_gen,
steps=args.epoch_steps,
verbose=args.verbose,
callbacks=[tensorboard])
print('### Model fit time (s): ', time() - fit_start_time)
def main(args):
# set the necessary list i.e. list of names of images to be read
train_list = pd.read_csv(args.train_list, header=None)
train_list_len = len(train_list)
epoch_steps = int(train_list_len / args.batch_size)
val_list = pd.read_csv(args.val_list, header=None)
val_list_len = len(val_list)
val_steps = int(val_list_len / args.batch_size)
# set the necessary directories
trainimg_dir = args.trainimg_dir
trainmsk_dir = args.trainmsk_dir
valimg_dir = args.valimg_dir
valmsk_dir = args.valmsk_dir
# Generate batch data for SGD
# NOTE: This helps control the batch size for each training and validation set
train_gen = data_gen_small(trainimg_dir, trainmsk_dir, train_list,
args.batch_size,
[args.input_shape[0], args.input_shape[1]],
args.n_labels)
val_gen = data_gen_small(valimg_dir, valmsk_dir, val_list, args.batch_size,
[args.input_shape[0], args.input_shape[1]],
args.n_labels)
# Create a model
if args.model == 'unet':
model = unet(\
args.input_shape, args.n_labels, args.kernel, \
args.pool, args.output_mode, args.gpus)
elif args.model == 'segnet':
model = segnet(\
args.input_shape, args.n_labels, args.kernel, \
args.pool, args.output_mode, args.gpus)
elif args.model == 'segunet':
model = segunet(\
args.input_shape, args.n_labels, args.kernel, \
args.pool, args.output_mode, args.gpus)
elif args.model == 'fastnet':
model = fastnet(\
args.input_shape, args.n_labels, args.kernel, \
args.pool, args.output_mode, args.gpus)
# Keras moodel summary
print(model.summary())
# TODO: Configure the model for training
optimizer = Adadelta(lr=args.lr,
rho=args.rho,
epsilon=args.epsilon,
decay=args.decay)
model.compile(loss=args.loss, optimizer=optimizer, metrics=[args.metrics])
# model.compile(loss=args.loss, optimizer=args.optimizer, metrics=[args.metrics])
# If pre-trained weights available, use those
if (args.initial_weights):
model.load_weights(args.initial_weights)
print('Initial weights loaded')
# Generate log for tensorboard (currently only useful parameters are added to the dir name)
log_dir = args.save_dir + \
'model_' + args.model + \
'_batch_size_' + str(args.batch_size) + \
'_epoch_steps_' + str(epoch_steps) + \
'_n_epochs_' + str(args.n_epochs) + \
'_lr_' + str(args.lr) + \
'_decay_' + str(args.decay) + \
'_labels_' + str(args.n_labels) + \
'/'
tensorboard = TensorBoard(log_dir=log_dir)
# Generate checkpoints
checkpoint = ModelCheckpoint(
log_dir + 'weights_at_epoch_{epoch:03d}.hdf5',
verbose=1,
save_best_only=False,
save_weights_only=True,
period=args.period) # Create 10 checkpoints only
# Train the model on data generated batch-by-batch by a Python generator
# NOTE: We use fit_generator because we do provide our generated dataset with specific batch size
fit_start_time = time()
model.fit_generator(train_gen,
steps_per_epoch=epoch_steps,
epochs=args.n_epochs,
validation_data=val_gen,
validation_steps=val_steps,
verbose=args.verbose,
callbacks=[checkpoint, tensorboard])
print('### Model fit time (s): ', time() - fit_start_time)
# # NOTE: Cannot save the whole model OR the model structure if the model is not Keras Sequential, it throws "AttributeError: 'NoneType' object has no attribute 'update'"
# # This is a bug in Keras (not sure about TensorFlow)
# # Therefore, the model strutuce must be generated every time it is required to be implemented outside of the current script
# model.save(log_dir + 'model.hdf5') # Model architecture and final weights
# Save final weights
model.save_weights(log_dir + 'weights_at_epoch_%03d.hdf5' %
(args.n_epochs))
print('Final model weights saved.')
def main(args):
# device number
if args.gpu_num:
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_num
print(os.environ["CUDA_VISIBLE_DEVICES"])
# set the necessary directories
train_dir = 'resources/train'
test_dir = 'resources/test'
train_list = []
train_list_file = "resources/train_list.txt"
val_list_file = "resources/val_list.txt"
with open(train_list_file, "r") as f:
for l in f:
train_list.append(l.replace("\n", ""))
val_list = []
with open(val_list_file, "r") as f:
for l in f:
val_list.append(l.replace("\n", ""))
with tf.Graph().as_default():
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.gpu_options.per_process_gpu_memory_fraction = 1.0
session = tf.Session(config=config)
KTF.set_session(session)
KTF.set_learning_phase(1)
# set callbacks
cp_cb = ModelCheckpoint(filepath='resources/checkpoints/checkpoint',
monitor='val_loss',
verbose=1,
save_best_only=True,
mode='auto',
period=2)
es_cb = EarlyStopping(monitor='val_loss',
patience=2,
verbose=1,
mode='auto')
tb_cb = TensorBoard(log_dir='resources/logs/', write_images=True)
csv_logger = CSVLogger('resources/logs/training.log')
# set generater
train_gen = data_gen_small('resources/train/', 'resources/train/',
train_list, args.batch_size,
[args.input_shape[0], args.input_shape[1]],
args.n_labels)
val_gen = data_gen_small('resources/val/', 'resources/val/', val_list,
args.batch_size,
[args.input_shape[0], args.input_shape[1]],
args.n_labels)
# set model
model = segunet(args.input_shape, args.n_labels, args.kernel,
args.pool_size, args.output_mode)
print(model.summary())
# compile model
model.compile(loss=args.loss,
optimizer=args.optimizer,
metrics=["accuracy"])
# fit with genarater
model.fit_generator(generator=train_gen,
steps_per_epoch=args.epoch_steps,
epochs=args.n_epochs,
validation_data=val_gen,
validation_steps=args.val_steps,
callbacks=[cp_cb, es_cb, tb_cb, csv_logger])
model.save_weights("resources/weights/weights_01.hdf5")
def main(args):
# device number
if args.gpu_num:
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_num
# set the necessary list
train_list = pd.read_csv(args.train_list, header=None)
val_list = pd.read_csv(args.val_list, header=None)
# set the necessary directories
trainimg_dir = args.trainimg_dir
trainmsk_dir = args.trainmsk_dir
valimg_dir = args.valimg_dir
valmsk_dir = args.valmsk_dir
# get old session
old_session = KTF.get_session()
with tf.Graph().as_default():
session = tf.Session('')
KTF.set_session(session)
KTF.set_learning_phase(1)
# set callbacks
cp_cb = ModelCheckpoint(filepath=fpath,
monitor='val_loss',
verbose=1,
save_best_only=True,
mode='auto',
period=2)
es_cb = EarlyStopping(monitor='val_loss',
patience=2,
verbose=1,
mode='auto')
tb_cb = TensorBoard(log_dir=args.log_dir, write_images=True)
# set generater
train_gen = data_gen_small(trainimg_dir, trainmsk_dir, train_list,
args.batch_size,
[args.input_shape[0], args.input_shape[1]],
args.n_labels)
val_gen = data_gen_small(valimg_dir, valmsk_dir, val_list,
args.batch_size,
[args.input_shape[0], args.input_shape[1]],
args.n_labels)
# set model
model = segunet(args.input_shape, args.n_labels, args.kernel,
args.pool_size, args.output_mode)
print(segunet.summary())
# compile model
model.compile(loss=args.loss,
optimizer=args.optimizer,
metrics=["accuracy"])
# fit with genarater
model.fit_generator(generator=train_gen,
steps_per_epoch=args.epoch_steps,
epochs=args.n_epochs,
validation_data=val_gen,
validation_steps=args.val_steps,
callbacks=[cp_cb, es_cb, tb_cb])
def __init__(self, input_shape = (512, 512, 3)):
self.model = segunet(input_shape, 20, 3, (2, 2), "softmax")
self.model.load_weights("resources/checkpoints/checkpoint_e02.hdf5")
self.input_shape = input_shape
self.cascade = cv2.CascadeClassifier("resources/haarcascade_fullbody.xml")
print(self.model.summary())