def train_gmn():
# ==> initial check
assert args.dataset == 'imagenet'
# ==> gpu configuration
ut.initialize_GPU(args)
# ==> set up model path and log path.
model_path, log_path = ut.set_path(args)
# ==> import library
import keras
import data_loader
import model_factory
import data_generator
# ==> get dataset information
trn_config = data_loader.get_config(args)
print('trn_config:', trn_config)
params = {
'cg': trn_config,
'processes': 12,
'batch_size': args.batch_size,
}
trn_gen, val_gen = data_generator.setup_generator(**params)
# ==> load model
gmn = model_factory.two_stream_matching_networks(trn_config)
gmn.summary()
# ==> attempt to load pre-trained model
if args.resume:
if os.path.isfile(args.resume):
gmn.load_weights(os.path.join(args.resume), by_name=True)
print('==> successfully loading the model: {}'.format(args.resume))
else:
print("==> no checkpoint found at '{}'".format(args.resume))
# ==> set up callbacks, e.g. lr schedule, tensorboard, save checkpoint.
normal_lr = keras.callbacks.LearningRateScheduler(ut.step_decay(args))
tbcallbacks = keras.callbacks.TensorBoard(log_dir=log_path,
histogram_freq=0,
write_graph=False,
write_images=False)
callbacks = [
keras.callbacks.ModelCheckpoint(os.path.join(model_path, 'model.h5'),
monitor='val_loss',
save_best_only=True,
mode='min'), normal_lr, tbcallbacks
]
gmn.fit_generator(trn_gen,
steps_per_epoch=600,
epochs=args.epochs,
validation_data=val_gen,
validation_steps=100,
callbacks=callbacks,
verbose=1)
def get_model():
# # ==> import library
import data_loader as data_loader
import model_factory as model_factory
# ==> get dataset information
global trn_config
trn_config = data_loader.get_config(args)
# ==> load networks
# well that adapt argument gotta be true otherwise you won't be able to load the weights
gmn = model_factory.two_stream_matching_networks(trn_config,
sync=False,
adapt=True)
gmn.load_weights(args.gmn_path, by_name=True)
# ==> print model summary
# gmn.summary()
return gmn
def adapt_gmn():
# ==> gpu configuration
ut.initialize_GPU(args)
# ==> set up model path and log path.
model_path, log_path = ut.set_path(args)
# ==> import library
import keras
import data_loader
import model_factory
import data_generator
# ==> get dataset information
trn_config = data_loader.get_config(args)
params = {'cg': trn_config, 'processes': 12, 'batch_size': args.batch_size}
trn_gen, val_gen = data_generator.setup_generator(**params)
# ==> load networks
gmn = model_factory.two_stream_matching_networks(trn_config,
sync=False,
adapt=False)
model = model_factory.two_stream_matching_networks(trn_config,
sync=False,
adapt=True)
# ==> attempt to load pre-trained model
if args.resume:
if os.path.isfile(args.resume):
model.load_weights(os.path.join(args.resume), by_name=True)
print('==> successfully loading the model: {}'.format(args.resume))
else:
print("==> no checkpoint found at '{}'".format(args.resume))
# ==> attempt to load pre-trained GMN
elif args.gmn_path:
if os.path.isfile(args.gmn_path):
gmn.load_weights(os.path.join(args.gmn_path), by_name=True)
print('==> successfully loading the model: {}'.format(
args.gmn_path))
else:
print("==> no checkpoint found at '{}'".format(args.gmn_path))
# ==> print model summary
model.summary()
# ==> transfer weights from gmn to new model (this step is slow, but can't seem to avoid it)
for i, layer in enumerate(gmn.layers):
if isinstance(layer, model.__class__):
for l in layer.layers:
weights = l.get_weights()
if len(weights) > 0:
#print('{}'.format(l.name))
model.layers[i].get_layer(l.name).set_weights(weights)
else:
weights = layer.get_weights()
if len(weights) > 0:
#print('{}'.format(layer.name))
model.get_layer(layer.name).set_weights(weights)
# ==> set up callbacks, e.g. lr schedule, tensorboard, save checkpoint.
normal_lr = keras.callbacks.LearningRateScheduler(ut.step_decay(args))
tbcallbacks = keras.callbacks.TensorBoard(log_dir=log_path,
histogram_freq=0,
write_graph=False,
write_images=False)
callbacks = [
keras.callbacks.ModelCheckpoint(os.path.join(model_path, 'model.h5'),
monitor='val_loss',
save_best_only=True,
mode='min'), normal_lr, tbcallbacks
]
model.fit_generator(trn_gen,
steps_per_epoch=600,
epochs=args.epochs,
validation_data=val_gen,
validation_steps=100,
callbacks=callbacks,
verbose=1)