config,
reuse=True)
if loss_type == 'g':
return losses['g_loss']
elif loss_type == 'd':
return losses['d_loss']
else:
raise ValueError('loss type is not supported.')
if __name__ == "__main__":
config = ng.Config('inpaint.yml')
if config.GPU_ID != -1:
ng.set_gpus(config.GPU_ID)
else:
ng.get_gpus(config.NUM_GPUS)
# training data
with open(config.DATA_FLIST[config.DATASET][0]) as f:
fnames = f.read().splitlines()
data = ng.data.DataFromFNames(fnames,
config.IMG_SHAPES,
random_crop=config.RANDOM_CROP)
images = data.data_pipeline(config.BATCH_SIZE)
# main model
model = InpaintCAModel()
g_vars, d_vars, losses = model.build_graph_with_losses(images,
config=config)
# validation images
if config.VAL:
with open(config.DATA_FLIST[config.DATASET][1]) as f:
val_fnames = f.read().splitlines()
default=None,
type=str,
help='filename list to use as dataset')
parser.add_argument('--height',
default=256,
type=int,
help='height of images in data flist')
parser.add_argument('--width',
default=256,
type=int,
help='width of images in data flist')
parser.add_argument('--blend', action='store_true')
if __name__ == "__main__":
ng.get_gpus(1, dedicated=False)
args = parser.parse_args()
config = ng.Config(args.config)
logger = logging.getLogger()
invert_mask = str2bool(args.invert_mask)
model = InpaintCAModel()
if args.image is not None:
image = cv2.imread(args.image)
h, w, _ = image.shape
assert image.shape == mask.shape
else:
if args.flist is None:
flist = config.DATA_FLIST[config.DATASET][1]
shapes = config.IMG_SHAPES
else:
help='The height of images should be defined, otherwise batch mode is not'
' supported.')
parser.add_argument(
'--image_width',
default=-1,
type=int,
help='The width of images should be defined, otherwise batch mode is not'
' supported.')
parser.add_argument('--checkpoint_dir',
default='',
type=str,
help='The directory of tensorflow checkpoint.')
if __name__ == "__main__":
FLAGS = ng.Config('inpaint.yml')
ng.get_gpus(1)
# os.environ['CUDA_VISIBLE_DEVICES'] =''
args = parser.parse_args()
sess_config = tf.ConfigProto()
sess_config.gpu_options.allow_growth = True
sess = tf.Session(config=sess_config)
model = InpaintCAModel()
input_image_ph = tf.placeholder(tf.float32,
shape=(1, args.image_height,
args.image_width * 2, 3))
output = model.build_server_graph(FLAGS, input_image_ph)
output = (output + 1.) * 127.5
output = tf.reverse(output, [-1])
output = tf.saturate_cast(output, tf.uint8)
core.seg_limit = 4000000 #// 10 # 보통 이게 더 큼
core.compl_limit = 1000000 #// 10 #
segnet_yml = 'segnet/seg48_4[553].yml' # segnet configuration
segnet_model_path = 'segnet/seg48_4[553].h5' # saved segnet model
complnet_ckpt_dir = 'v2_180923' # saved complnets directory
#--------------------------------------------
# for segnet
with open(segnet_yml, 'r') as f:
config = yaml.load(f)
# for complnet
complnet = InpaintCAModel('v2')
ng.get_gpus(1, False) #TODO: 단 한 번만 호출할 것.
#--------------------------------------------
dilate_kernel = core.rect5
if not os.path.isdir('./cleaned'):
os.mkdir('./cleaned')
app = QtWidgets.QApplication(sys.argv)
screen = app.primaryScreen()
print('Screen: %s' % screen.name())
size = screen.size()
print('Size: %d x %d' % (size.width(), size.height()))
rect = screen.availableGeometry()
print('Available: %d x %d' % (rect.width(), rect.height()))
MainWindow = MyMainScreen()
MainWindow.show()
#app.exec_()