def build_infer_graph(self, batch_data, config, bbox=None, name='val'):
"""
"""
config.MAX_DELTA_HEIGHT = 0
config.MAX_DELTA_WIDTH = 0
if bbox is None:
bbox = random_bbox(config)
mask = bbox2mask(bbox, config, name=name + 'mask_c')
batch_pos = batch_data / 127.5 - 1.
edges = None
batch_incomplete = batch_pos * (1. - mask)
# inpaint
x1, x2, offset_flow = self.build_inpaint_net(batch_incomplete,
mask,
config,
reuse=True,
training=False,
padding=config.PADDING)
if config.PRETRAIN_COARSE_NETWORK:
batch_predicted = x1
else:
batch_predicted = x2
# apply mask and reconstruct
batch_complete = batch_predicted * mask + batch_incomplete * (1. -
mask)
# global image visualization
viz_img = [batch_pos, batch_incomplete, batch_complete]
if offset_flow is not None:
viz_img.append(
resize(offset_flow,
scale=4,
func=tf.image.resize_nearest_neighbor))
return batch_complete
def build_infer_graph(self, FLAGS, batch_data, bbox=None, name='val'):
"""
"""
if FLAGS.guided:
batch_data, edge = batch_data
edge = edge[:, :, :, 0:1] / 255.
edge = tf.cast(edge > FLAGS.edge_threshold, tf.float32)
regular_mask = bbox2mask(FLAGS, bbox, name='mask_c')
irregular_mask = brush_stroke_mask(FLAGS, name='mask_c')
mask = tf.cast(
tf.logical_or(
tf.cast(irregular_mask, tf.bool),
tf.cast(regular_mask, tf.bool),
), tf.float32)
batch_pos = batch_data / 127.5 - 1.
batch_incomplete = batch_pos * (1. - mask)
if FLAGS.guided:
edge = edge * mask
xin = tf.concat([batch_incomplete, edge], axis=3)
else:
xin = batch_incomplete
# inpaint
x1, x2, offset_flow = self.build_inpaint_net(xin,
mask,
reuse=True,
training=False,
padding=FLAGS.padding)
batch_predicted = x2
# apply mask and reconstruct
batch_complete = batch_predicted * mask + batch_incomplete * (1. -
mask)
# global image visualization
if FLAGS.guided:
viz_img = [batch_pos, batch_incomplete + edge, batch_complete]
else:
viz_img = [batch_pos, batch_incomplete, batch_complete]
if offset_flow is not None:
viz_img.append(
resize(offset_flow, scale=4, func=tf.image.resize_bilinear))
images_summary(tf.concat(viz_img, axis=2),
name + '_raw_incomplete_complete', FLAGS.viz_max_out)
return batch_complete
def build_graph_with_losses(self,
batch_data,
config,
training=True,
reuse=False):
batch_pos = batch_data / 127.5 - 1.
# generate mask, 1 represents masked point
bbox = random_bbox(config)
mask = bbox2mask(bbox, config, name='mask_c')
batch_incomplete = batch_pos * (1. - mask)
x1, x2, offset_flow = self.build_inpaint_net(batch_incomplete,
mask,
config,
reuse=reuse,
training=training,
padding=config.PADDING)
if config.PRETRAIN_COARSE_NETWORK:
batch_predicted = x1
else:
batch_predicted = x2
losses = {}
# apply mask and complete image
batch_complete = batch_predicted * mask + batch_incomplete * (1. -
mask)
# local patches
local_patch_batch_pos = local_patch(batch_pos, bbox)
local_patch_batch_predicted = local_patch(batch_predicted, bbox)
local_patch_x1 = local_patch(x1, bbox)
local_patch_x2 = local_patch(x2, bbox)
local_patch_batch_complete = local_patch(batch_complete, bbox)
local_patch_mask = local_patch(mask, bbox)
l1_alpha = config.COARSE_L1_ALPHA
losses['l1_loss'] = l1_alpha * tf.reduce_mean(
tf.abs(local_patch_batch_pos - local_patch_x1) *
spatial_discounting_mask(config))
if not config.PRETRAIN_COARSE_NETWORK:
losses['l1_loss'] += tf.reduce_mean(
tf.abs(local_patch_batch_pos - local_patch_x2) *
spatial_discounting_mask(config))
losses['ae_loss'] = l1_alpha * tf.reduce_mean(
tf.abs(batch_pos - x1) * (1. - mask))
if not config.PRETRAIN_COARSE_NETWORK:
losses['ae_loss'] += tf.reduce_mean(
tf.abs(batch_pos - x2) * (1. - mask))
losses['ae_loss'] /= tf.reduce_mean(1. - mask)
# gan
batch_pos_neg = tf.concat([batch_pos, batch_complete], axis=0)
# local deterministic patch
local_patch_batch_pos_neg = tf.concat(
[local_patch_batch_pos, local_patch_batch_complete], 0)
if config.GAN_WITH_MASK:
batch_pos_neg = tf.concat([
batch_pos_neg,
tf.tile(mask, [config.BATCH_SIZE * 2, 1, 1, 1])
],
axis=3)
# wgan with gradient penalty
if config.GAN == 'wgan_gp':
# seperate gan
pos_neg_local, pos_neg_global = self.build_wgan_discriminator(
local_patch_batch_pos_neg,
batch_pos_neg,
training=training,
reuse=reuse)
pos_local, neg_local = tf.split(pos_neg_local, 2)
pos_global, neg_global = tf.split(pos_neg_global, 2)
# wgan loss
g_loss_local, d_loss_local = gan_wgan_loss(pos_local,
neg_local,
name='gan/local_gan')
g_loss_global, d_loss_global = gan_wgan_loss(pos_global,
neg_global,
name='gan/global_gan')
losses[
'g_loss'] = config.GLOBAL_WGAN_LOSS_ALPHA * g_loss_global + g_loss_local
losses['d_loss'] = d_loss_global + d_loss_local
# gp
interpolates_local = random_interpolates(
local_patch_batch_pos, local_patch_batch_complete)
interpolates_global = random_interpolates(batch_pos,
batch_complete)
dout_local, dout_global = self.build_wgan_discriminator(
interpolates_local, interpolates_global, reuse=True)
# apply penalty
penalty_local = gradients_penalty(interpolates_local,
dout_local,
mask=local_patch_mask)
penalty_global = gradients_penalty(interpolates_global,
dout_global,
mask=mask)
losses['gp_loss'] = config.WGAN_GP_LAMBDA * (penalty_local +
penalty_global)
losses['d_loss'] = losses['d_loss'] + losses['gp_loss']
if config.PRETRAIN_COARSE_NETWORK:
losses['g_loss'] = 0
else:
losses['g_loss'] = config.GAN_LOSS_ALPHA * losses['g_loss']
losses['g_loss'] += config.L1_LOSS_ALPHA * losses['l1_loss']
if config.AE_LOSS:
losses['g_loss'] += config.AE_LOSS_ALPHA * losses['ae_loss']
g_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
'inpaint_net')
d_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
'discriminator')
return g_vars, d_vars, losses
def build_graph_with_losses(self,
batch_data,
config,
training=True,
summary=False,
reuse=False):
batch_pos = batch_data / 127.5 - 1.
# generate mask, 1 represents masked point
bbox = random_bbox(config)
mask = bbox2mask(bbox, config, name='mask_c')
batch_incomplete = batch_pos * (1. - mask)
x1, x2, offset_flow = self.build_inpaint_net(batch_incomplete,
mask,
config,
reuse=reuse,
training=training,
padding=config.PADDING)
if config.PRETRAIN_COARSE_NETWORK:
batch_predicted = x1
logger.info('Set batch_predicted to x1.')
else:
batch_predicted = x2
logger.info('Set batch_predicted to x2.')
losses = {}
# apply mask and complete image
batch_complete = batch_predicted * mask + batch_incomplete * (1. -
mask)
# local patches
local_patch_batch_pos = local_patch(batch_pos, bbox)
local_patch_batch_predicted = local_patch(batch_predicted, bbox)
local_patch_x1 = local_patch(x1, bbox)
local_patch_x2 = local_patch(x2, bbox)
local_patch_batch_complete = local_patch(batch_complete, bbox)
local_patch_mask = local_patch(mask, bbox)
l1_alpha = config.COARSE_L1_ALPHA
losses['l1_loss'] = l1_alpha * tf.reduce_mean(
tf.abs(local_patch_batch_pos - local_patch_x1) *
spatial_discounting_mask(config))
if not config.PRETRAIN_COARSE_NETWORK:
losses['l1_loss'] += tf.reduce_mean(
tf.abs(local_patch_batch_pos - local_patch_x2) *
spatial_discounting_mask(config))
losses['ae_loss'] = l1_alpha * tf.reduce_mean(
tf.abs(batch_pos - x1) * (1. - mask))
if not config.PRETRAIN_COARSE_NETWORK:
losses['ae_loss'] += tf.reduce_mean(
tf.abs(batch_pos - x2) * (1. - mask))
losses['ae_loss'] /= tf.reduce_mean(1. - mask)
if summary:
scalar_summary('losses/l1_loss', losses['l1_loss'])
scalar_summary('losses/ae_loss', losses['ae_loss'])
viz_img = [batch_pos, batch_incomplete, batch_complete]
if offset_flow is not None:
viz_img.append(
resize(offset_flow,
scale=4,
func=tf.image.resize_nearest_neighbor))
images_summary(tf.concat(viz_img, axis=2),
'raw_incomplete_predicted_complete',
config.VIZ_MAX_OUT)
# gan
batch_pos_neg = tf.concat([batch_pos, batch_complete], axis=0)
# local deterministic patch
local_patch_batch_pos_neg = tf.concat(
[local_patch_batch_pos, local_patch_batch_complete], 0)
if config.GAN_WITH_MASK:
batch_pos_neg = tf.concat([
batch_pos_neg,
tf.tile(mask, [config.BATCH_SIZE * 2, 1, 1, 1])
],
axis=3)
# wgan with gradient penalty
if config.GAN == 'wgan_gp':
# seperate gan
pos_neg_local, pos_neg_global = self.build_wgan_discriminator(
local_patch_batch_pos_neg,
batch_pos_neg,
training=training,
reuse=reuse)
pos_local, neg_local = tf.split(pos_neg_local, 2)
pos_global, neg_global = tf.split(pos_neg_global, 2)
# wgan loss
g_loss_local, d_loss_local = gan_wgan_loss(pos_local,
neg_local,
name='gan/local_gan')
g_loss_global, d_loss_global = gan_wgan_loss(pos_global,
neg_global,
name='gan/global_gan')
losses[
'g_loss'] = config.GLOBAL_WGAN_LOSS_ALPHA * g_loss_global + g_loss_local
losses['d_loss'] = d_loss_global + d_loss_local
# gp
interpolates_local = random_interpolates(
local_patch_batch_pos, local_patch_batch_complete)
interpolates_global = random_interpolates(batch_pos,
batch_complete)
dout_local, dout_global = self.build_wgan_discriminator(
interpolates_local, interpolates_global, reuse=True)
# apply penalty
penalty_local = gradients_penalty(interpolates_local,
dout_local,
mask=local_patch_mask)
penalty_global = gradients_penalty(interpolates_global,
dout_global,
mask=mask)
losses['gp_loss'] = config.WGAN_GP_LAMBDA * (penalty_local +
penalty_global)
losses['d_loss'] = losses['d_loss'] + losses['gp_loss']
if summary and not config.PRETRAIN_COARSE_NETWORK:
gradients_summary(g_loss_local,
batch_predicted,
name='g_loss_local')
gradients_summary(g_loss_global,
batch_predicted,
name='g_loss_global')
scalar_summary('convergence/d_loss', losses['d_loss'])
scalar_summary('convergence/local_d_loss', d_loss_local)
scalar_summary('convergence/global_d_loss', d_loss_global)
scalar_summary('gan_wgan_loss/gp_loss', losses['gp_loss'])
scalar_summary('gan_wgan_loss/gp_penalty_local', penalty_local)
scalar_summary('gan_wgan_loss/gp_penalty_global',
penalty_global)
if summary and not config.PRETRAIN_COARSE_NETWORK:
# summary the magnitude of gradients from different losses w.r.t. predicted image
gradients_summary(losses['g_loss'], batch_predicted, name='g_loss')
gradients_summary(losses['g_loss'], x1, name='g_loss_to_x1')
gradients_summary(losses['g_loss'], x2, name='g_loss_to_x2')
gradients_summary(losses['l1_loss'], x1, name='l1_loss_to_x1')
gradients_summary(losses['l1_loss'], x2, name='l1_loss_to_x2')
gradients_summary(losses['ae_loss'], x1, name='ae_loss_to_x1')
gradients_summary(losses['ae_loss'], x2, name='ae_loss_to_x2')
if config.PRETRAIN_COARSE_NETWORK:
losses['g_loss'] = 0
else:
losses['g_loss'] = config.GAN_LOSS_ALPHA * losses['g_loss']
losses['g_loss'] += config.L1_LOSS_ALPHA * losses['l1_loss']
logger.info('Set L1_LOSS_ALPHA to %f' % config.L1_LOSS_ALPHA)
logger.info('Set GAN_LOSS_ALPHA to %f' % config.GAN_LOSS_ALPHA)
if config.AE_LOSS:
losses['g_loss'] += config.AE_LOSS_ALPHA * losses['ae_loss']
logger.info('Set AE_LOSS_ALPHA to %f' % config.AE_LOSS_ALPHA)
g_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
'inpaint_net')
d_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
'discriminator')
return g_vars, d_vars, losses
def build_graph_with_losses(self,
FLAGS,
batch_data,
training=True,
summary=False,
reuse=False):
if FLAGS.guided:
batch_data, edge = batch_data
edge = edge[:, :, :, 0:1] / 255.
edge = tf.cast(edge > FLAGS.edge_threshold, tf.float32)
batch_pos = batch_data / 127.5 - 1.
# generate mask, 1 represents masked point
bbox = random_bbox(FLAGS)
regular_mask = bbox2mask(FLAGS, bbox, name='mask_c')
irregular_mask = brush_stroke_mask(FLAGS, name='mask_c')
mask = tf.cast(
tf.logical_or(
tf.cast(irregular_mask, tf.bool),
tf.cast(regular_mask, tf.bool),
), tf.float32)
batch_incomplete = batch_pos * (1. - mask)
if FLAGS.guided:
edge = edge * mask
xin = tf.concat([batch_incomplete, edge], axis=3)
else:
xin = batch_incomplete
x1, x2, offset_flow = self.build_inpaint_net(xin,
mask,
reuse=reuse,
training=training,
padding=FLAGS.padding)
batch_predicted = x2
losses = {}
# apply mask and complete image
batch_complete = batch_predicted * mask + batch_incomplete * (1. -
mask)
# local patches
losses['ae_loss'] = FLAGS.l1_loss_alpha * tf.reduce_mean(
tf.abs(batch_pos - x1))
losses['ae_loss'] += FLAGS.l1_loss_alpha * tf.reduce_mean(
tf.abs(batch_pos - x2))
if summary:
scalar_summary('losses/ae_loss', losses['ae_loss'])
if FLAGS.guided:
viz_img = [batch_pos, batch_incomplete + edge, batch_complete]
else:
viz_img = [batch_pos, batch_incomplete, batch_complete]
if offset_flow is not None:
viz_img.append(
resize(offset_flow, scale=4,
func=tf.image.resize_bilinear))
images_summary(tf.concat(viz_img, axis=2),
'raw_incomplete_predicted_complete',
FLAGS.viz_max_out)
# gan
batch_pos_neg = tf.concat([batch_pos, batch_complete], axis=0)
if FLAGS.gan_with_mask:
batch_pos_neg = tf.concat([
batch_pos_neg,
tf.tile(mask, [FLAGS.batch_size * 2, 1, 1, 1])
],
axis=3)
if FLAGS.guided:
# conditional GANs
batch_pos_neg = tf.concat(
[batch_pos_neg, tf.tile(edge, [2, 1, 1, 1])], axis=3)
# wgan with gradient penalty
if FLAGS.gan == 'sngan':
pos_neg = self.build_gan_discriminator(batch_pos_neg,
training=training,
reuse=reuse)
pos, neg = tf.split(pos_neg, 2)
g_loss, d_loss = gan_hinge_loss(pos, neg)
losses['g_loss'] = g_loss
losses['d_loss'] = d_loss
else:
raise NotImplementedError('{} not implemented.'.format(FLAGS.gan))
if summary:
# summary the magnitude of gradients from different losses w.r.t. predicted image
gradients_summary(losses['g_loss'], batch_predicted, name='g_loss')
gradients_summary(losses['g_loss'], x2, name='g_loss_to_x2')
# gradients_summary(losses['ae_loss'], x1, name='ae_loss_to_x1')
gradients_summary(losses['ae_loss'], x2, name='ae_loss_to_x2')
losses['g_loss'] = FLAGS.gan_loss_alpha * losses['g_loss']
if FLAGS.ae_loss:
losses['g_loss'] += losses['ae_loss']
g_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
'inpaint_net')
d_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
'discriminator')
return g_vars, d_vars, losses