return return_img
if not os.path.exists(FLAGS.saved_prediction):
os.mkdir(FLAGS.saved_prediction)
val_data = input_data.read_val_data(rgb_mean=FLAGS.rgb_mean,
crop_height=FLAGS.crop_height,
crop_width=FLAGS.crop_width,
classes=FLAGS.classes,
ignore_label=FLAGS.ignore_label,
scales=FLAGS.scales)
test_data = input_data.read_test_data(rgb_mean=FLAGS.rgb_mean,
crop_height=FLAGS.crop_height,
crop_width=FLAGS.crop_width,
classes=FLAGS.classes,
ignore_label=FLAGS.ignore_label,
scales=FLAGS.scales)
with tf.name_scope("input"):
x = tf.placeholder(tf.float32,
[FLAGS.batch_size, FLAGS.height, FLAGS.width, 3],
name='x_input')
y = tf.placeholder(tf.int32, [FLAGS.batch_size, FLAGS.height, FLAGS.width],
name='ground_truth')
_, logits = PSPNet.PSPNet(x,
is_training=False,
output_stride=FLAGS.output_stride,
pre_trained_model=FLAGS.pretrained_model_path,
saved_prediction_val_gray = './pred/val_gray'
saved_prediction_test_color = './pred/test_color'
saved_prediction_test_gray = './pred/test_gray'
VAL_LIST = input_data.VAL_LIST
ANNOTATION_PATH = input_data.ANNOTATION_PATH
val_num = 1449
test_num = 1456
if not os.path.exists('./pred'):
os.mkdir('./pred')
val_data = input_data.read_val_data()
test_data = input_data.read_test_data()
with tf.name_scope("input"):
x = tf.placeholder(tf.float32, [BATCH_SIZE, None, None, 3], name='x_input')
y = tf.placeholder(tf.int32, [BATCH_SIZE, None, None], name='ground_truth')
logits = deeplab_model.deeplab_v3_plus(x,
is_training=False,
output_stride=8,
pre_trained_model=PRETRAINED_MODEL_PATH)
with tf.name_scope('prediction_and_miou'):
prediction = tf.argmax(logits, axis=-1, name='predictions')