parser.add_argument('--max_steps', type=int, default=1500)
parser.add_argument('--learning_rate', type=float, default=1e-4)
FLAGS, unparsed = parser.parse_known_args()
return FLAGS, unparsed
FLAGS, unparsed = parse_args()
slim = tf.contrib.slim
tf.reset_default_graph()
is_training_placeholder = tf.placeholder(tf.bool)
batch_size = FLAGS.batch_size
image_tensor_train, orig_img_tensor_train, annotation_tensor_train = inputs(
FLAGS.dataset_train, train=True, batch_size=batch_size, num_epochs=1e4)
image_tensor_val, orig_img_tensor_val, annotation_tensor_val = inputs(
FLAGS.dataset_val, train=False, num_epochs=1e4)
image_tensor, orig_img_tensor, annotation_tensor = tf.cond(
is_training_placeholder,
true_fn=lambda:
(image_tensor_train, orig_img_tensor_train, annotation_tensor_train),
false_fn=lambda:
(image_tensor_val, orig_img_tensor_val, annotation_tensor_val))
feed_dict_to_use = {is_training_placeholder: True}
upsample_factor = 8
number_of_classes = 21
parser.add_argument('--train_dir', type=str)
parser.add_argument('--dataset_train', type=str)
parser.add_argument('--batch_size', type=int, default=16)
parser.add_argument('--max_steps', type=int, default=1500)
parser.add_argument('--learning_rate', type=float, default=1e-4)
FLAGS, unparsed = parser.parse_known_args()
return FLAGS, unparsed
FLAGS, unparsed = parse_args()
number_of_classes = 21
# Define input
image_tensor, orig_img_tensor, annotation_tensor = inputs(
FLAGS.dataset_train,
train=True,
batch_size=FLAGS.batch_size,
num_epochs=1e4)
# Define loss
cross_entropy_loss = vgg16_fcn_loss(image_tensor, annotation_tensor,
number_of_classes)
global_step, train_step = optimizer(
cross_entropy_loss,
FLAGS.learning_rate,
global_step=tf.train.get_or_create_global_step())
log_folder = FLAGS.train_dir
if not os.path.exists(log_folder):
os.makedirs(log_folder)
def parse_args(check=True):
parser = argparse.ArgumentParser()
parser.add_argument('--train_dir', type=str)
parser.add_argument('--eval_dir', type=str)
parser.add_argument('--dataset_val', type=str)
parser.add_argument('--num_pics', type=int, default=10)
FLAGS, unparsed = parser.parse_known_args()
return FLAGS, unparsed
FLAGS, unparsed = parse_args()
number_of_classes=21
# Define network
image_tensor, orig_img_tensor, annotation_tensor = inputs(FLAGS.dataset_val, train=False, num_epochs=1e4)
pred,probabilities = vgg16_fcn_pred(image_tensor,number_of_classes)
eval_dir = FLAGS.eval_dir
if not os.path.exists(eval_dir):
os.makedirs(eval_dir)
sess_config = tf.ConfigProto()
sess_config.gpu_options.allow_growth = True
init_op = tf.global_variables_initializer()
init_local_op = tf.local_variables_initializer()
with tf.Session(config=sess_config) as sess:
# Run the initializers.
parser.add_argument('--learning_rate', type=float, default=1e-4)
FLAGS, unparsed = parser.parse_known_args()
return FLAGS, unparsed
FLAGS, unparsed = parse_args()
slim = tf.contrib.slim
tf.reset_default_graph()
is_training_placeholder = tf.placeholder(tf.bool)
batch_size = FLAGS.batch_size
image_tensor_train, orig_img_tensor_train, annotation_tensor_train = inputs(FLAGS.dataset_train, train=True, batch_size=batch_size, num_epochs=1e4)
image_tensor_val, orig_img_tensor_val, annotation_tensor_val = inputs(FLAGS.dataset_val, train=False, num_epochs=1e4)
image_tensor, orig_img_tensor, annotation_tensor = tf.cond(is_training_placeholder,
true_fn=lambda: (image_tensor_train, orig_img_tensor_train, annotation_tensor_train),
false_fn=lambda: (image_tensor_val, orig_img_tensor_val, annotation_tensor_val))
feed_dict_to_use = {is_training_placeholder: True}
upsample_factor = 16
number_of_classes = 21
log_folder = os.path.join(FLAGS.output_dir, 'train')
vgg_checkpoint_path = FLAGS.checkpoint_path
