def build_infer_graph(self, batch_data, config, name='val'):
config.MAX_DELTA_HEIGHT = 0
config.MAX_DELTA_WIDTH = 0
mask = bbox2mask(config, name=name + 'mask_c')
batch_pos = batch_data / 127.5 - 1.
edges = None
batch_incomplete = batch_pos * (1. - mask)
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
logger.info('Set batch_predicted to x1.')
else:
batch_predicted = x2
logger.info('Set batch_predicted to x2.')
batch_complete = batch_predicted * mask + batch_incomplete * (1. -
mask)
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),
name + '_raw_incomplete_complete', config.VIZ_MAX_OUT)
return batch_complete
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
x2, offset_flow = self.build_inpaint_net(
batch_incomplete, mask, config, reuse=True,
training=False, padding=config.PADDING)
batch_predicted = x2
logger.info('Set batch_predicted to 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))
images_summary(
tf.concat(viz_img, axis=2),
name+'_raw_incomplete_complete', config.VIZ_MAX_OUT)
return batch_complete
def train_step(iter_idx):
with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:
batch_pos = next(train_iter)
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)
# mask is 0-1
batch_incomplete = batch_pos * (1. - mask)
xin = batch_incomplete
x1, x2, offset_flow = G(xin, mask, training=True)
batch_predicted = x2
losses = {}
batch_complete = batch_predicted * \
mask + batch_incomplete * (1. - mask)
losses['ae_loss'] = FLAGS['l1_loss_alpha'] * \
tf.reduce_mean(input_tensor=tf.abs(batch_pos - x1))
losses['ae_loss'] += FLAGS['l1_loss_alpha'] * \
tf.reduce_mean(input_tensor=tf.abs(batch_pos - x2))
batch_pos_neg = tf.concat([batch_pos, batch_complete], axis=0)
batch_pos_neg = tf.concat([
batch_pos_neg,
tf.tile(mask, [FLAGS['batch_size'] * 2, 1, 1, 1])
],
axis=3)
# SNGAN
pos_neg = D(batch_pos_neg, training=True)
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
losses['g_loss'] = FLAGS['gan_loss_alpha'] * losses['g_loss']
losses['g_loss'] += losses['ae_loss']
# return losses
gradients_of_generator = gen_tape.gradient(losses['g_loss'],
G.trainable_variables)
gradients_of_discriminator = disc_tape.gradient(
losses['d_loss'], D.trainable_variables)
g_optimizer.apply_gradients(
zip(gradients_of_generator, G.trainable_variables))
d_optimizer.apply_gradients(
zip(gradients_of_discriminator, D.trainable_variables))
# tensorboard
if iter_idx > 0 and iter_idx % FLAGS['viz_max_out'] == 0:
with train_summary_writer.as_default():
tf.summary.scalar('g_loss', losses['g_loss'], step=iter_idx)
tf.summary.scalar('d_loss', losses['d_loss'], step=iter_idx)
img = tf.reshape(batch_complete[0], (-1, 256, 256, 3))
tf.summary.image("train", img, step=iter_idx)
def build_infer_graph(self,
batch_data,
config,
bbox=None,
name='val',
exclusionmask=None):
"""
"""
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
logger.info('Set batch_predicted to x1.')
else:
batch_predicted = x2
logger.info('Set batch_predicted to x2.')
# apply mask and reconstruct
batch_complete = batch_predicted * mask + batch_incomplete * (1. -
mask)
# global image visualization
img_size = [dim for dim in batch_incomplete.shape]
img_size[2] = 5
border = tf.zeros(tf.TensorShape(img_size))
viz_img = [
border, batch_pos, border, batch_incomplete, border,
batch_complete, border
]
if not config.PRETRAIN_COARSE_NETWORK:
batch_complete_coarse = x1 * mask + batch_incomplete * (1. - mask)
viz_img.append(batch_complete_coarse)
if offset_flow is not None:
scale = 2 << len(offset_flow)
for flow in offset_flow:
viz_img.append(
resize(flow,
scale=scale,
func=tf.image.resize_nearest_neighbor))
viz_img.append(border)
scale >>= 1
images_summary(tf.concat(viz_img, axis=2),
name + '_raw_incomplete_complete', config.VIZ_MAX_OUT)
return batch_complete
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
if not config.ADD_GRADIENT_BRANCH:
x1, x2, offset_flow = self.build_inpaint_net(
batch_incomplete,
mask,
config,
reuse=True,
training=False,
padding=config.PADDING)
else:
x1, x2, fake_gb, gb, 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
logger.info('Set batch_predicted to x1.')
else:
batch_predicted = x2
logger.info('Set batch_predicted to 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 config.ADD_GRADIENT_BRANCH:
gb_gt = self.get_grad.get_gradient_tf(batch_pos)
batch_complete_gb = fake_gb * mask + gb_gt * (1. - mask)
viz_img.extend([gb_gt, batch_complete_gb])
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),
name + '_raw_incomplete_complete', config.VIZ_MAX_OUT)
return batch_complete
def _create_mask(config, edge=None):
# num_class = 1 # only for person.
size = config.IMG_SHAPES[:2]
if config.FREE_FORM:
mask = free_form_mask(np, 1, size)
else:
bbox = random_bbox(config)
mask = bbox2mask(bbox, 1, config, np)
if edge is not None:
edge = mask * (edge[None, :1] / 255.)
mask = np.concatenate([mask, edge], axis=1)
return mask[0]
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)
mask = brush_stroke_mask(name='mask_c')
regular_mask = bbox2mask(bbox, name='mask_c')
irregular_mask = brush_stroke_mask(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 evaluation(trainer):
it = trainer.updater.get_iterator('test')
batch_data = it.next()
batch_data = self.xp.array(batch_data)
# generate mask, 1 represents masked point
bbox = (config.HEIGHT // 2, config.WIDTH // 2, config.HEIGHT,
config.WIDTH)
config.MAX_DELTA_HEIGHT = 0
config.MAX_DELTA_WIDTH = 0
if bbox is None:
bbox = random_bbox(config)
mask = bbox2mask(bbox, batch_data.shape[0], config, self.xp)
batch_pos = batch_data / 127.5 - 1.
batch_incomplete = batch_pos * (1. - mask)
# inpaint
x1, x2, offset_flow = self.inpaintnet(batch_incomplete, mask,
config)
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)
# visualization
viz_img = [batch_pos, batch_incomplete + mask, batch_complete.data]
if offset_flow is not None:
viz_img.append(F.unpooling_2d(offset_flow, 4).data)
batch_w = len(viz_img)
batch_h = viz_img[0].shape[0]
viz_img = self.xp.concatenate(viz_img, axis=0)
viz_img = batch_postprocess_images(viz_img, batch_w, batch_h)
viz_img = cuda.to_cpu(viz_img)
Image.fromarray(viz_img).save(test_image_folder + "/iter_" +
str(trainer.updater.iteration) +
".jpg")
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
def build_graph_with_losses(self,
batch_data,
config,
training=True,
summary=False,
reuse=False):
batch_pos = batch_data / 127.5 - 1.
mask = bbox2mask(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 = {}
batch_complete = batch_predicted * mask + batch_incomplete * (1. -
mask)
local_patch_batch_pos = local_patch(batch_pos, mask)
local_patch_batch_predicted = local_patch(batch_predicted, mask)
local_patch_x1 = local_patch(x1, mask)
local_patch_x2 = local_patch(x2, mask)
local_patch_batch_complete = local_patch(batch_complete, mask)
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)
batch_pos_neg = tf.concat([batch_pos, batch_complete], axis=0)
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)
if config.GAN == 'snpatch_gan':
pos_neg = self.build_SNGAN_discriminator(local_patch_batch_pos_neg,
training=training,
reuse=reuse)
pos, neg = tf.split(pos_neg, 2)
sn_gloss, sn_dloss = self.gan_hinge_loss(pos,
neg,
name="gan/hinge_loss")
losses['g_loss'] = config.GLOBAL_WGAN_LOSS_ALPHA * sn_gloss
losses['d_loss'] = sn_dloss
interpolates = random_interpolates(a1, a2)
dout = self.build_SNGAN_discriminator(interpolates, reuse=True)
penalty = gradients_penalty(interpolates, dout, mask=mask)
losses['gp_loss'] = config.WGAN_GP_LAMBDA * penalty
losses['d_loss'] = losses['d_loss'] + losses['gp_loss']
if summary and not config.PRETRAIN_COARSE_NETWORK:
gradients_summary(sn_gloss,
batch_predicted,
name='g_loss_local')
scalar_summary('convergence/d_loss', losses['d_loss'])
scalar_summary('convergence/local_d_loss', sn_dloss)
scalar_summary('gan_wgan_loss/gp_loss', losses['gp_loss'])
scalar_summary('gan_wgan_loss/gp_penalty_local', penalty)
if summary and not config.PRETRAIN_COARSE_NETWORK:
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, 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,
batch_data,
config,
training=True,
summary=False,
reuse=False,
exclusionmask=None,
mask=None):
batch_pos = batch_data / 127.5 - 1.
# generate mask, 1 represents masked point
use_local_patch = False
if mask is None:
bbox = random_bbox(config)
mask = bbox2mask(bbox, config, name='mask_c')
if config.GAN == 'wgan_gp':
use_local_patch = True
else:
#bbox = (0, 0, config.IMG_SHAPES[0], config.IMG_SHAPES[1])
mask = tf.cast(tf.less(0.5, mask[:, :, :, 0:1]), tf.float32)
if config.INVERT_MASK:
mask = 1 - mask
batch_incomplete = batch_pos * (1. - mask)
if exclusionmask is not None:
if config.INVERT_EXCLUSIONMASK:
loss_mask = tf.cast(tf.less(0.5, exclusionmask[:, :, :, 0:1]),
tf.float32) #keep white parts
else:
loss_mask = tf.cast(tf.less(exclusionmask[:, :, :, 0:1], 0.5),
tf.float32) #keep black parts
batch_incomplete = batch_incomplete * loss_mask
batch_pos = batch_pos * loss_mask
x1, x2, offset_flow = self.build_inpaint_net(
batch_incomplete,
mask,
config,
reuse=reuse,
training=training,
padding=config.PADDING,
exclusionmask=exclusionmask)
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)
if exclusionmask is not None:
batch_complete = batch_complete * loss_mask
l1_alpha = config.COARSE_L1_ALPHA
# local patches
if use_local_patch:
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)
losses['l1_loss'] = l1_alpha * tf.reduce_mean(
tf.abs(local_patch_batch_pos - local_patch_x1) *
spatial_discounting_mask(config) *
(loss_mask if exclusionmask is not None else 1))
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) *
(loss_mask if exclusionmask is not None else 1))
losses['ae_loss'] = l1_alpha * tf.reduce_mean(
tf.abs(batch_pos - x1) * (1. - mask) *
(loss_mask if exclusionmask is not None else 1))
if not config.PRETRAIN_COARSE_NETWORK:
losses['ae_loss'] += tf.reduce_mean(
tf.abs(batch_pos - x2) * (1. - mask) *
(loss_mask if exclusionmask is not None else 1))
losses['ae_loss'] /= tf.reduce_mean(1. - mask)
else:
losses['l1_loss'] = l1_alpha * tf.reduce_mean(
tf.abs(batch_pos - x1) *
(loss_mask if exclusionmask is not None else 1))
if not config.PRETRAIN_COARSE_NETWORK:
losses['l1_loss'] += tf.reduce_mean(
tf.abs(batch_pos - x2) *
(loss_mask if exclusionmask is not None else 1))
if summary:
scalar_summary('losses/l1_loss', losses['l1_loss'])
if use_local_patch:
scalar_summary('losses/ae_loss', losses['ae_loss'])
img_size = [dim for dim in batch_incomplete.shape]
img_size[2] = 5
border = tf.zeros(tf.TensorShape(img_size))
viz_img = [
batch_pos, border, batch_incomplete, border, batch_complete,
border
]
if not config.PRETRAIN_COARSE_NETWORK:
batch_complete_coarse = x1 * mask + batch_incomplete * (1. -
mask)
viz_img.append(batch_complete_coarse)
viz_img.append(border)
if offset_flow is not None:
scale = 2 << len(offset_flow)
for flow in offset_flow:
viz_img.append(
resize(flow,
scale=scale,
func=tf.image.resize_nearest_neighbor))
viz_img.append(border)
scale >>= 1
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
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':
if not use_local_patch:
raise Exception('wgan_gp requires global and local patch')
local_patch_batch_pos_neg = tf.concat(
[local_patch_batch_pos, local_patch_batch_complete], 0)
# 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)
elif config.GAN == 'sngan':
if use_local_patch:
raise Exception(
'sngan incompatible with global and local patch')
pos_neg = self.build_sngan_discriminator(batch_pos_neg,
name='discriminator',
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
if summary and not config.PRETRAIN_COARSE_NETWORK:
gradients_summary(g_loss, batch_predicted, name='g_loss')
scalar_summary('convergence/d_loss', losses['d_loss'])
else:
losses['g_loss'] = 0
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')
if use_local_patch:
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 and use_local_patch:
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 get_loss(self, batch_data):
config = self.config
batch_pos = batch_data / 127.5 - 1
bbox = random_bbox(config)
mask = bbox2mask(bbox, batch_data.shape[0], config, self.xp)
batch_incomplete = batch_pos * (1 - mask)
x1, x2, offset_flow = self.inpaintnet(batch_incomplete, mask, config)
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_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 * F.mean(
F.absolute(local_patch_batch_pos - local_patch_x1) *
spatial_discounting_mask(config, self.xp))
if not config.PRETRAIN_COARSE_NETWORK:
losses['l1_loss'] += F.mean(
F.absolute(local_patch_batch_pos - local_patch_x2) *
spatial_discounting_mask(config, self.xp))
losses['ae_loss'] = l1_alpha * F.mean(
F.absolute(batch_pos - x1) * (1. - mask))
if not config.PRETRAIN_COARSE_NETWORK:
losses['ae_loss'] += F.mean(
F.absolute(batch_pos - x2) * (1. - mask))
losses['ae_loss'] /= F.mean(1. - mask)
# gan
batch_pos_neg = F.concat([batch_pos, batch_complete], axis=0)
# local deterministic patch
local_patch_batch_pos_neg = F.concat(
[local_patch_batch_pos, local_patch_batch_complete], 0)
if config.GAN_WITH_MASK:
batch_pos_neg = F.concat([batch_pos_neg, mask], axis=1)
# wgan with gradient penalty
if config.GAN == 'wgan_gp':
# seperate gan
pos_neg_local, pos_neg_global = self.discriminator(
local_patch_batch_pos_neg, batch_pos_neg)
pos_local, neg_local = F.split_axis(pos_neg_local, 2, axis=0)
pos_global, neg_global = F.split_axis(pos_neg_global, 2, axis=0)
# wgan loss
g_loss_local, d_loss_local = gan_wgan_loss(pos_local, neg_local)
g_loss_global, d_loss_global = gan_wgan_loss(
pos_global, neg_global)
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.discriminator(
interpolates_local, interpolates_global)
# 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']
return losses
