def main(args):
# 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
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)
model = segnet(args.input_shape, args.n_labels, args.kernel,
args.pool_size, args.output_mode)
print(model.summary())
model.compile(loss=args.loss,
optimizer=args.optimizer,
metrics=["accuracy"])
model.fit_generator(train_gen,
steps_per_epoch=args.epoch_steps,
epochs=args.n_epochs,
validation_data=val_gen,
validation_steps=args.val_steps)
model.save_weights(args.save_dir + str(args.n_epochs) + ".hdf5")
print("sava weight done..")
def train(args):
train_list = range(args.train_size)
val_list = range(args.val_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
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)
model = segnet(args.input_shape, args.n_labels, args.kernel,
args.pool_size, args.output_mode, args.model_number)
print(model.summary())
with open(args.save_dir + 'model' + str(args.model_number) + '.txt',
'w') as fh:
model.summary(print_fn=lambda x: fh.write(x + '\n'))
model.compile(loss=args.loss,
optimizer=args.optimizer,
metrics=["accuracy"])
print("Model Compiled")
tensorboard = TensorBoard(log_dir='logs/{}'.format(time()))
model.fit_generator(train_gen,
steps_per_epoch=args.epoch_steps,
epochs=args.n_epochs,
validation_data=val_gen,
validation_steps=args.val_steps,
callbacks=[tensorboard])
model.save(args.save_dir + args.new_model_name + ".hdf5")
print("Model Saved")
K.set_learning_phase(0)
session = K.get_session()
init = tf.global_variables_initializer()
session.run(init)
frozen_graph = freeze_session(
session, output_names=[out.op.name for out in model.outputs])
print([out.op.name for out in model.outputs])
tf.train.write_graph(frozen_graph,
args.save_dir,
args.new_model_name + ".pb",
as_text=False)
session.close()
file1 = open(args.save_dir + "recent.txt", "w+")
file1.write(args.new_model_name)
file1.close()
def main(args):
# set the necessary list
train_file = open(args.train_list, "r")
train_list = train_file.readlines()
random.shuffle(train_list)
val_file = open(args.val_list, "r")
val_list = val_file.readlines()
test_file = open(args.test_list, "r")
test_list = test_file.readlines()
train_gen = data_gen_small(
train_list,
args.batch_size,
[args.input_shape[0], args.input_shape[1]],
args.n_labels,
)
val_gen = data_gen_small(
val_list,
args.batch_size,
[args.input_shape[0], args.input_shape[1]],
args.n_labels,
)
model = segnet(args.input_shape, args.n_labels, args.kernel,
args.pool_size, args.output_mode)
print(model.summary())
model.compile(loss=args.loss,
optimizer=Adam(lr=0.001),
metrics=["accuracy"])
if args.resume:
print("Load Model: " + args.resume)
model.load_weights(args.resume)
model.fit_generator(
train_gen,
steps_per_epoch=args.epoch_steps,
epochs=args.n_epochs,
validation_data=val_gen,
validation_steps=args.val_steps,
)
model.save_weights(args.save_dir + str(args.n_epochs) + ".hdf5")
print("save weight done..")
test_gen = test_data_generator(
test_list,
#args.batch_size,
[args.input_shape[0], args.input_shape[1]],
args.n_labels,
)
model.evaluate(test_gen)
def main(args):
# 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
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)
segnet = CreateSegNet(args.input_shape, args.n_labels, args.kernel,
args.pool_size, args.output_mode)
#print(segnet.summary())
segnet.compile(loss=args.loss,
optimizer=args.optimizer,
metrics=["categorical_accuracy"])
segnet.load_weights('../weights/MTI_SegNet.hdf5')
segnet.fit_generator(train_gen,
steps_per_epoch=args.epoch_steps,
epochs=args.n_epochs,
validation_data=val_gen,
validation_steps=args.val_steps,
workers=1,
max_queue_size=20)
segnet.save_weights("../weights/MTI_SegNet.hdf5")
print("saving weights done..")
def main(args):
# 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
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)
segnet = CreateSegNet(args.input_shape, args.n_labels, args.kernel,
args.pool_size, args.output_mode)
print(segnet.summary())
segnet.compile(loss=args.loss,
optimizer=args.optimizer,
metrics=["accuracy"])
segnet.fit_generator(train_gen,
steps_per_epoch=args.epoch_steps,
epochs=args.n_epochs,
validation_data=val_gen,
validation_steps=args.val_steps)
segnet.save_weights("../LIP/pretrained/LIP_SegNet" + str(args.n_epochs) +
".hdf5")
print("sava weight done..")
json_string = segnet.to_json()
open("../LIP/pretrained/LIP_SegNet.json", "w").write(json_string)
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):
# device number
if args.gpu_num:
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
# 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=args.log_dir,
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
pspnet = PSPNet50(input_shape=args.input_shape,
n_labels=args.n_labels,
output_mode=args.output_mode,
upsample_type=args.upsample_type)
print(pspnet.summary())
# compile model
pspnet.compile(loss=args.loss,
optimizer=args.optimizer,
metrics=["accuracy"])
# fit with genarater
pspnet.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 main(args):
# 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)
# class weights
classes = ['background', 'hat', 'hair', 'glove', 'sunglasses', 'upperclothes',
'dress', 'coat', 'socks', 'pants', 'jumpsuits', 'scarf', 'skirt',
'face', 'leftArm', 'rightArm', 'leftLeg', 'rightLeg', 'leftShoe','rightShoe']
if args.class_weights:
"""
class_weights = {0:1, 1:40, 2:1, 3:114, 4:151, 5:3, 6:53, 7:7, 8:165, 9:7, 10:106,
11:249, 12:150, 13:1, 14:1, 15:1, 16:1, 17:1, 18:114, 19:118}
"""
class_weights = [1, 40, 1, 114, 151, 3, 53, 7, 165, 7, 106, 249, 150, 1, 1, 1, 1, 1, 114, 118]
# set callbacks
fpath = "./pretrained_class_weights/LIP_PSPNet50_class_weights{epoch:02d}.hdf5"
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="./pretrained_class_weights", 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
pspnet = PSPNet50(
input_shape=args.input_shape,
n_labels=args.n_labels,
output_mode=args.output_mode,
upsample_type=args.upsample_type)
print(pspnet.summary())
# compile model
pspnet.compile(
loss=args.loss,
optimizer=args.optimizer,
metrics=["accuracy"])
# fit with genarater
pspnet.fit_generator(
generator=train_gen,
steps_per_epoch=args.epoch_steps,
epochs=args.n_epochs,
validation_data=val_gen,
validation_steps=args.val_steps,
class_weight=class_weights,
callbacks=[cp_cb, es_cb, tb_cb])
# save model
with open("./pretrained_class_weights/LIP_PSPNet50.json", "w") as json_file:
json_file.write(json.dumps(json.loads(pspnet.to_json()), indent=2))
print("save json model done...")
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):
# set the necessary list
#train_list = pd.read_csv(args.train_list, header=None)
train_list = pd.read_csv(
'gdrive/My Drive/Research/TTLAB/AI_For_Earth/Problem_2/test_2/train.csv'
)
#val_list = pd.read_csv(args.val_list, header=None)
# set the necessary directories
#trainimg_dir = args.trainimg_dir
trainimg_dir = 'gdrive/My Drive/Research/TTLAB/AI_For_Earth/Problem_2/multiclassification/data/SequoiaMulti_30/train_partition_images/'
trainmsk_dir = 'gdrive/My Drive/Research/TTLAB/AI_For_Earth/Problem_2/multiclassification/data/SequoiaMulti_30/train_partition_ground_truth/'
#trainmsk_dir = args.trainmsk_dir
#valimg_dir = args.valimg_dir
#valmsk_dir = args.valmsk_dir
train_gen = data_gen_small(
trainimg_dir,
trainmsk_dir,
train_list,
args.batch_size,
#[args.input_shape[0], args.input_shape[1]],
(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,
)
'''
model = segnet(args.input_shape, args.n_labels, args.kernel,
args.pool_size, args.output_mode)
print(model.summary())
model.compile(loss=args.loss,
optimizer=args.optimizer,
metrics=["accuracy"])
addtional = 50
train_resume = True
weights_path = 'gdrive/My Drive/Research/TTLAB/AI_For_Earth/Problem_2/test_2/' + str(
600) + ".hdf5"
print(weights_path)
if train_resume:
model.load_weights(weights_path)
print("\nTraining resumed...")
else:
print("\nTraining started from scratch... ")
model.fit_generator(
train_gen,
steps_per_epoch=args.epoch_steps,
epochs=args.n_epochs,
#validation_data=val_gen,
#validation_steps=args.val_steps,
)
model.save_weights(
'gdrive/My Drive/Research/TTLAB/AI_For_Earth/Problem_2/test_2/' +
str(650) + ".hdf5")
print("sava weight done..")
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="./pretrained_class_weights",
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
segunet = CreateSegUNet(args.input_shape, args.n_labels, args.kernel,
args.pool_size, args.output_mode)
print(segunet.summary())
# compile model
segunet.compile(loss=args.loss,
optimizer=args.optimizer,
metrics=["accuracy"])
include_top=False,
input_shape=(256, 256, 3),
weights='imagenet')
#model.summary()
train_list = pd.read_csv('./train.csv', header=None)
val_list = pd.read_csv('./test.csv', header=None)
trainimg_dir = './training_dataset'
trainmsk_dir = './training_mask'
valimg_dir = './validating_dataset'
valmsk_dir = './validating_mask'
x, y = data_gen_small(trainimg_dir,
trainmsk_dir,
6,
batch_size=1,
dims=[256, 256],
n_labels=2)
#print(x.shape)
x1, y1 = data_gen_small(valimg_dir,
valmsk_dir,
3,
batch_size=1,
dims=[256, 256],
n_labels=2)
stochastic = SGD(lr=0.001, momentum=0.9, decay=0.0, nesterov=False)
callbacks = [
ModelCheckpoint('bestmodel.h5', monitor='val_loss', save_best_only=True)
]
model.compile(loss="categorical_crossentropy",
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")
