def train_RNet(base_dir,
prefix,
log_dir,
model_checkpoint,
end_epoch,
display,
lr,
optimizer='adam'):
"""
train PNet
:param dataset_dir: tfrecord path
:param prefix:
:param end_epoch:
:param display:
:param lr:
:return:
"""
net_factory = R_Net
train(net_factory,
prefix,
end_epoch,
base_dir,
log_dir,
display=display,
base_lr=lr,
ckpt=model_checkpoint,
optimizer=optimizer)
def train_PNet(base_dir, prefix, end_epoch, display, lr, restore):
"""
:param base_dir: '../../DATA/imglists/PNet'
:param prefix: '../ckpt/%s_model/PNet_landmark/PNet' % model_name
:param end_epoch:
:param display:
:param lr:
:return:
"""
net_factory = P_Net
train(net_factory, prefix, end_epoch, base_dir, display=display, base_lr=lr, restore=restore)
def train_PNet(base_dir, prefix, end_epoch, display, lr):
"""
train PNet
:param dataset_dir: tfrecord path
:param prefix:
:param end_epoch: max epoch for training
:param display:
:param lr: learning rate
:return:
"""
net_factory = P_Net
train(net_factory,prefix, end_epoch, base_dir, display=display, base_lr=lr)
def train_ONet(base_dir, prefix, end_epoch, display, lr):
"""
train PNet
:param dataset_dir: tfrecord path
:param prefix:
:param end_epoch:
:param display:
:param lr:
:return:
"""
net_factory = O_Net
train(net_factory, prefix, end_epoch, base_dir, display=display, base_lr=lr)
def train_PNet(tfrecord_path, model_save_path, max_epoch, display, lr):
""" train PNet
:param tfrecord_path: tfrecord训练数据路径
:param model_save_path: 模型保存路径
:param max_epoch: 最大训练epoch
:param display: 日志打印
:param lr: learning rate
:return:
"""
net_factory = P_Net
train(net_factory,
model_save_path,
max_epoch,
tfrecord_path,
display=display,
base_lr=lr)
def train_PNet(base_dir, prefix, end_epoch, display, lr):
"""
train PNet
Parameters:
---------------
base_dir:
dataset_dir: tfrecord路径
prefix: model路径
end_epoch: 训练最大轮次数
display: 每训练display个step输出训练状态
lr: 学习率
"""
net_factory = P_Net
train(net_factory,
prefix,
end_epoch,
base_dir,
display=display,
base_lr=lr)
