# training data
FLAGS = ng.Config('inpaint.yml')
img_shapes = FLAGS.img_shapes
with open(FLAGS.data_flist[FLAGS.dataset][0]) as f:
fnames = f.read().splitlines()
if FLAGS.guided:
fnames = [(fname, fname[:-4] + '_edge.jpg') for fname in fnames]
img_shapes = [img_shapes, img_shapes]
data = ng.data.DataFromFNames(fnames,
img_shapes,
random_crop=FLAGS.random_crop,
nthreads=FLAGS.num_cpus_per_job)
images = data.data_pipeline(FLAGS.batch_size)
# main model
model = InpaintCAModel()
g_vars, d_vars, losses = model.build_graph_with_losses(FLAGS, images)
# validation images
if FLAGS.val:
with open(FLAGS.data_flist[FLAGS.dataset][1]) as f:
val_fnames = f.read().splitlines()
if FLAGS.guided:
val_fnames = [(fname, fname[:-4] + '_edge.jpg')
for fname in val_fnames]
# progress monitor by visualizing static images
for i in range(FLAGS.static_view_size):
static_fnames = val_fnames[i:i + 1]
static_images = ng.data.DataFromFNames(
static_fnames,
img_shapes,
nthreads=1,
random_crop=FLAGS.random_crop).data_pipeline(1)
if count == 1:
fnames = f.read().splitlines()
elif count == 2:
fnames = [(l.split(' ')[0], l.split(' ')[1]) for l in f.read().splitlines()]
elif count == 3:
fnames = [(l.split(' ')[0], l.split(' ')[1], l.split(' ')[2]) for l in f.read().splitlines()]
else:
print('invalid data count')
exit()
data = ng.data.DataFromFNames(
fnames, config.IMG_SHAPES, random_crop=config.RANDOM_CROP, gamma=config.GAMMA, exposure=config.EXPOSURE, random_flip = config.RANDOM_FLIP)
images = data.data_pipeline(config.BATCH_SIZE)
# main model
model = InpaintCAModel()
g_vars, d_vars, losses = model.build_graph_with_losses(
images[0], config=config, exclusionmask=images[exclusionmask_index] if config.EXC_MASKS else None, mask=None if config.GEN_MASKS else images[mask_index])
# validation images
if config.VAL:
with open(config.DATA_FLIST[config.DATASET][1]) as f:
val_fnames = [(l.split(' ')[0], l.split(' ')[1]) for l in f.read().splitlines()]
# progress monitor by visualizing static images
for i in range(config.STATIC_VIEW_SIZE):
static_fnames = val_fnames[i:i+1]
static_images = ng.data.DataFromFNames(
static_fnames, config.IMG_SHAPES, nthreads=1,
random_crop=config.RANDOM_CROP, random_flip=config.RANDOM_FLIP).data_pipeline(1)
static_inpainted_images = model.build_static_infer_graph(
static_images[0], config, name='static_view/%d' % i, exclusionmask=images[exclusionmask_index] if config.EXC_MASKS else None)
# training settings
lr = tf.get_variable(
'lr', shape=[], trainable=False,
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()
# progress monitor by visualizing static images
for i in range(config.STATIC_VIEW_SIZE):
static_fnames = val_fnames[i:i + 1]
static_images = ng.data.DataFromFNames(
static_fnames,
config.IMG_SHAPES,
nthreads=1,
random_crop=config.RANDOM_CROP).data_pipeline(1)
static_inpainted_images = model.build_static_infer_graph(
static_images, config, name='static_view/%d' % i)
if __name__ == "__main__":
# training data
FLAGS = ng.Config('inpaint.yml')
img_shapes = FLAGS.img_shapes
# Read flist input and mask image
with open(FLAGS.data_flist[FLAGS.dataset][0]) as f:
fnames_input = f.read().splitlines()
train_fnames = [(fname, fname[:-4]+'mask.png') for fname in fnames_input]
data = ng.data.DataFromFNames(
train_fnames, img_shapes, random_crop=FLAGS.random_crop,
nthreads=FLAGS.num_cpus_per_job)
batch_data = data.data_pipeline(FLAGS.batch_size)
# main model
model = InpaintCAModel()
g_vars, d_vars, losses = model.build_graph_with_losses(FLAGS, batch_data)
# validation images
with open(FLAGS.data_flist[FLAGS.dataset][1]) as f:
val_fnames = f.read().splitlines()
val_fnames = [(fname, fname[:-4] + 'mask.png') for fname in val_fnames]
batch_val = ng.data.DataFromFNames(
val_fnames, img_shapes, nthreads=FLAGS.num_cpus_per_job,
random_crop = FLAGS.random_crop).data_pipeline(FLAGS.batch_size)
batch_val_complete = model.build_infer_graph(FLAGS, batch_val)
num_iters_val = len(val_fnames)//FLAGS.batch_size
# training settings
lr = tf.get_variable(
'lr', shape=[], trainable=False,
initializer=tf.constant_initializer(1e-4))
d_optimizer = tf.train.AdamOptimizer(lr, beta1=0.5, beta2=0.999)
g_optimizer = d_optimizer
