test_imgs_with_guide += [(None, None,
os.path.join(baseDirLabel, name, label_id,
'00000.png'),
os.path.join(baseDirImg, name, '00001.jpg'))]
test_imgs_with_guide += [
(None, None,
os.path.join(resDirLabel, name, label_id,
prev_frame[:-4] + '.png'),
os.path.join(baseDirImg, name, frame))
for prev_frame, frame in zip(test_frames[1:-1], test_frames[2:])
]
# Define Dataset
dataset = Dataset(train_imgs_with_guide,
test_imgs_with_guide,
args,
data_aug=True)
with tf.Graph().as_default():
with tf.device('/gpu:' + str(args.gpu_id)):
if not args.only_testing:
max_training_iters = args.training_iters
save_step = args.save_iters
display_step = args.display_iters
logs_path = os.path.join(args.model_save_path, name,
label_id)
global_step = tf.Variable(0,
name='global_step',
trainable=False)
osmn.train_finetune(
dataset,
os.path.join(baseDirLabel, name,
test_frames[0][:-4] + '.png'), None,
None)]
test_imgs_with_guide += [(None, None,
os.path.join(baseDirLabel, name,
test_frames[0][:-4] + '.png'),
os.path.join(baseDirImg, name, test_frames[1]))]
test_imgs_with_guide += [
(None, None, os.path.join(resDirLabel, name, prev_frame[:-4] + '.png'),
os.path.join(baseDirImg, name, frame))
for prev_frame, frame in zip(test_frames[1:-1], test_frames[2:])
]
# Define Dataset
dataset = Dataset([], test_imgs_with_guide, args)
## default config
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.allow_soft_placement = True
config.gpu_options.per_process_gpu_memory_fraction = 0.9
# Test the network
with tf.Graph().as_default():
with tf.device('/gpu:' + str(args.gpu_id)):
checkpoint_path = args.whole_model_path
osmn.test(dataset,
args,
checkpoint_path,
args.result_path,