def train(self):
# dsn_2_loss + dsn_3_loss + dsn_4_loss + dsn_5_loss + main_loss
with tf.Graph().as_default():
global_step = tf.Variable(0, name='global_step', trainable=False)
learning_rate = tf.train.piecewise_constant(global_step, self.boundaries, self.values)
osvos.train_parent(self.dataset, self.imagenet_ckpt, 1, learning_rate, self.model_result_path,
self.train_iters[0], self.save_step, self.display_step, global_step,
iter_mean_grad=self.iter_mean_grad, test_image_path=None, ckpt_name=self.ckpt_name)
# 0.5 * (dsn_2_loss + dsn_3_loss + dsn_4_loss + dsn_5_loss) + main_loss
with tf.Graph().as_default():
global_step = tf.Variable(self.train_iters[0], name='global_step', trainable=False)
learning_rate = tf.train.piecewise_constant(global_step, self.boundaries, self.values)
osvos.train_parent(self.dataset, self.imagenet_ckpt, 2, learning_rate, self.model_result_path,
self.train_iters[1], self.save_step, self.display_step, global_step,
iter_mean_grad=self.iter_mean_grad, resume_training=True,
test_image_path=None, ckpt_name=self.ckpt_name)
# main_loss
with tf.Graph().as_default():
global_step = tf.Variable(self.train_iters[1], name='global_step', trainable=False)
learning_rate = tf.train.piecewise_constant(global_step, self.boundaries, self.values)
osvos.train_parent(self.dataset, self.imagenet_ckpt, 3, learning_rate, self.model_result_path,
self.train_iters[2], self.save_step, self.display_step, global_step,
iter_mean_grad=self.iter_mean_grad, resume_training=True,
test_image_path=None, ckpt_name=self.ckpt_name)
pass
ini_learning_rate = 1e-8
boundaries = [10000, 15000, 25000, 30000, 40000]
values = [ini_learning_rate, ini_learning_rate * 0.1, ini_learning_rate, ini_learning_rate * 0.1, ini_learning_rate,
ini_learning_rate * 0.1]
# Define Dataset
train_file = 'train_parent.txt'
dataset = Dataset(train_file, None, './DAVIS', store_memory=store_memory, data_aug=data_aug)
# Train the network
with tf.Graph().as_default():
with tf.device('/gpu:' + str(gpu_id)):
global_step = tf.Variable(0, name='global_step', trainable=False)
learning_rate = tf.train.piecewise_constant(global_step, boundaries, values)
osvos.train_parent(dataset, imagenet_ckpt, 1, learning_rate, logs_path, max_training_iters_1, save_step,
display_step, global_step, iter_mean_grad=iter_mean_grad, test_image_path=test_image,
ckpt_name='OSVOS_parent')
with tf.Graph().as_default():
with tf.device('/gpu:' + str(gpu_id)):
global_step = tf.Variable(max_training_iters_1, name='global_step', trainable=False)
learning_rate = tf.train.piecewise_constant(global_step, boundaries, values)
osvos.train_parent(dataset, imagenet_ckpt, 2, learning_rate, logs_path, max_training_iters_2, save_step,
display_step, global_step, iter_mean_grad=iter_mean_grad, resume_training=True,
test_image_path=test_image, ckpt_name='OSVOS_parent')
with tf.Graph().as_default():
with tf.device('/gpu:' + str(gpu_id)):
global_step = tf.Variable(max_training_iters_2, name='global_step', trainable=False)
learning_rate = tf.train.piecewise_constant(global_step, boundaries, values)
osvos.train_parent(dataset, imagenet_ckpt, 3, learning_rate, logs_path, max_training_iters_3, save_step,
ini_learning_rate = 1e-8
boundaries = [10000, 15000, 25000, 30000, 40000]
values = [ini_learning_rate, ini_learning_rate * 0.1, ini_learning_rate, ini_learning_rate * 0.1, ini_learning_rate,
ini_learning_rate * 0.1]
# Define Dataset
train_file = 'train_parent.txt'
dataset = Dataset(train_file, None, './DAVIS', store_memory=store_memory, data_aug=data_aug)
# Train the network
with tf.Graph().as_default():
with tf.device('/gpu:' + str(gpu_id)):
global_step = tf.Variable(0, name='global_step', trainable=False)
learning_rate = tf.train.piecewise_constant(global_step, boundaries, values)
osvos.train_parent(dataset, imagenet_ckpt, 1, learning_rate, logs_path, max_training_iters_1, save_step,
display_step, global_step, iter_mean_grad=iter_mean_grad, test_image_path=test_image,
ckpt_name='OSVOS_parent')
with tf.Graph().as_default():
with tf.device('/gpu:' + str(gpu_id)):
global_step = tf.Variable(max_training_iters_1, name='global_step', trainable=False)
learning_rate = tf.train.piecewise_constant(global_step, boundaries, values)
osvos.train_parent(dataset, imagenet_ckpt, 2, learning_rate, logs_path, max_training_iters_2, save_step,
display_step, global_step, iter_mean_grad=iter_mean_grad, resume_training=True,
test_image_path=test_image, ckpt_name='OSVOS_parent')
with tf.Graph().as_default():
with tf.device('/gpu:' + str(gpu_id)):
global_step = tf.Variable(max_training_iters_2, name='global_step', trainable=False)
learning_rate = tf.train.piecewise_constant(global_step, boundaries, values)
osvos.train_parent(dataset, imagenet_ckpt, 3, learning_rate, logs_path, max_training_iters_3, save_step,
# Define Dataset
# smoke dataset
train_file = 'smoke_train_list.txt'
dataset = Dataset(train_file, None, './dataset/smoke_dataset/*',
store_memory=store_memory, data_aug=data_aug, flow_given=True)
# davis 2016 dataset
# train_file = 'train_parent_davis.txt'
# dataset = Dataset(train_file, None, './dataset/DAVIS/', store_memory=store_memory, data_aug=data_aug)
# Train the network
with tf.Graph().as_default():
with tf.device('/gpu:' + str(gpu_id)):
global_step = tf.Variable(0, name='global_step', trainable=False)
learning_rate = tf.train.piecewise_constant(global_step, boundaries, values)
osvos.train_parent(dataset, pretrained_ckpt, 1, learning_rate, logs_path, max_training_iters_1, save_step,
display_step, global_step, iter_mean_grad=iter_mean_grad, test_image_path=test_image,
ckpt_name='OSVOS_parent', backbone=backbone_arch, batch_size=batch_size)
with tf.Graph().as_default():
with tf.device('/gpu:' + str(gpu_id)):
global_step = tf.Variable(max_training_iters_1, name='global_step', trainable=False)
learning_rate = tf.train.piecewise_constant(global_step, boundaries, values)
osvos.train_parent(dataset, pretrained_ckpt, 2, learning_rate, logs_path, max_training_iters_2, save_step,
display_step, global_step, iter_mean_grad=iter_mean_grad, resume_training=True,
test_image_path=test_image, ckpt_name='OSVOS_parent')
with tf.Graph().as_default():
with tf.device('/gpu:' + str(gpu_id)):
global_step = tf.Variable(max_training_iters_2, name='global_step', trainable=False)
learning_rate = tf.train.piecewise_constant(global_step, boundaries, values)
osvos.train_parent(dataset, pretrained_ckpt, 3, learning_rate, logs_path, max_training_iters_3, save_step,