def train_with_data_generator(dataset_root_dir=GAN_DATA_ROOT_DIR,
weights_file=None):
net_name = 'o_net'
batch_size = BATCH_SIZE
epochs = ONET_EPOCHS
learning_rate = ONET_LEARNING_RATE
dataset_dir = os.path.join(dataset_root_dir, net_name)
pos_dataset_path = os.path.join(dataset_dir, 'pos_shuffle.h5')
neg_dataset_path = os.path.join(dataset_dir, 'neg_shuffle.h5')
part_dataset_path = os.path.join(dataset_dir, 'part_shuffle.h5')
landmarks_dataset_path = os.path.join(dataset_dir, 'landmarks_shuffle.h5')
data_generator = DataGenerator(pos_dataset_path,
neg_dataset_path,
part_dataset_path,
landmarks_dataset_path,
batch_size,
im_size=NET_SIZE['o_net'])
data_gen = data_generator.generate()
steps_per_epoch = data_generator.steps_per_epoch()
callbacks, model_file = create_callbacks_model_file(net_name, epochs)
_o_net = train_o_net_with_data_generator(data_gen,
steps_per_epoch,
initial_epoch=0,
epochs=epochs,
lr=learning_rate,
callbacks=callbacks,
weights_file=weights_file)
_o_net.save_weights(model_file)
def train_with_data_generator(dataset_root_dir=GAN_DATA_ROOT_DIR,
model_file=model_file,
weights_file=None):
batch_size = 64 * 7
epochs = 30
learning_rate = 0.001
pos_dataset_path = os.path.join(dataset_root_dir,
'pos_shuffle_%s.h5' % (net_name))
neg_dataset_path = os.path.join(dataset_root_dir,
'neg_shuffle_%s.h5' % (net_name))
part_dataset_path = os.path.join(dataset_root_dir,
'part_shuffle_%s.h5' % (net_name))
landmarks_dataset_path = os.path.join(
dataset_root_dir, 'landmarks_shuffle_%s.h5' % (net_name))
data_generator = DataGenerator(pos_dataset_path,
neg_dataset_path,
part_dataset_path,
landmarks_dataset_path,
batch_size,
im_size=12)
data_gen = data_generator.generate()
steps_per_epoch = data_generator.steps_per_epoch()
if net_name == 'Pnet':
_net = Pnet()
elif net_name == 'Rnet':
_net = Rnet()
else:
_net = Onet()
_net_model = _net.model(training=True)
_net_model.summary()
if weights_file is not None:
_net_model.load_weights(weights_file)
#sgd = SGD(lr=0.005, momentum=0.8)
#_p_net_model.compile(optimizer=sgd, loss=_p_net.loss_func, metrics=[_p_net.accuracy, _p_net.recall])
_net_model.compile(Adam(lr=learning_rate),
loss=_net.loss_func,
metrics=[_net.accuracy, _net.recall])
_net_model.fit_generator(data_gen,
steps_per_epoch=steps_per_epoch,
initial_epoch=0,
epochs=epochs)
_net_model.save_weights(model_file)
def main():
filepath = os.path.dirname(os.path.abspath(__file__))
dataset_root_dir = filepath + '/data/traindata/'
pos_dataset_path = os.path.join(dataset_root_dir, 'pos_shuffle.h5')
neg_dataset_path = os.path.join(dataset_root_dir, 'neg_shuffle.h5')
part_dataset_path = os.path.join(dataset_root_dir, 'part_shuffle.h5')
landmarks_dataset_path = os.path.join(dataset_root_dir, 'landmarks_shuffle.h5')
data_generator = DataGenerator(pos_dataset_path, neg_dataset_path, part_dataset_path, landmarks_dataset_path, 64*7, im_size=12)
data_gen = data_generator.generate()
for item in data_gen:
print(item[1][:,:1])
exit(0)
