/
staged_model.py
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/
staged_model.py
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# Copyright 2019 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Neural re-rerendering in the wild.
Implementation of the staged training pipeline.
"""
from options import FLAGS as opts
import losses
import networks
import tensorflow as tf
import utils
def create_computation_graph(x_in, x_gt, x_app=None, arch_type='pggan',
use_appearance=True):
"""Create the models and the losses.
Args:
x_in: 4D tensor, batch of conditional input images in NHWC format.
x_gt: 2D tensor, batch ground-truth images in NHWC format.
x_app: 4D tensor, batch of input appearance images.
Returns:
Dictionary of placeholders and TF graph functions.
"""
# ---------------------------------------------------------------------------
# Build models/networks
# ---------------------------------------------------------------------------
rerenderer = networks.RenderingModel(arch_type, use_appearance)
app_enc = rerenderer.get_appearance_encoder()
discriminator = networks.MultiScaleDiscriminator(
'd_model', opts.appearance_nc, num_scales=3, nf=64, n_layers=3,
get_fmaps=False)
# ---------------------------------------------------------------------------
# Forward pass
# ---------------------------------------------------------------------------
if opts.use_appearance:
z_app, _, _ = app_enc(x_app)
else:
z_app = None
y = rerenderer(x_in, z_app)
# ---------------------------------------------------------------------------
# Losses
# ---------------------------------------------------------------------------
w_loss_gan = opts.w_loss_gan
w_loss_recon = opts.w_loss_vgg if opts.use_vgg_loss else opts.w_loss_l1
# compute discriminator logits
disc_real_featmaps = discriminator(x_gt, tf.compat.v1.slice(x_in, [0, 0, 0, 0], [-1, -1, -1, opts.deep_buffer_nc]))
disc_fake_featmaps = discriminator(y, tf.compat.v1.slice(x_in, [0, 0, 0, 0], [-1, -1, -1, opts.deep_buffer_nc]))
# discriminator loss
loss_d_real = losses.multiscale_discriminator_loss(disc_real_featmaps, True)
loss_d_fake = losses.multiscale_discriminator_loss(disc_fake_featmaps, False)
loss_d = loss_d_real + loss_d_fake
# generator loss
loss_g_gan = losses.multiscale_discriminator_loss(disc_fake_featmaps, True)
if opts.use_vgg_loss:
vgg_layers = ['conv%d_2' % i for i in range(1, 6)] # conv1 through conv5
vgg_layer_weights = [1./32, 1./16, 1./8, 1./4, 1.]
vgg_loss = losses.PerceptualLoss(y, x_gt, [512, 512, 3], vgg_layers,
vgg_layer_weights) # NOTE: shouldn't hardcode image size!
loss_g_recon = vgg_loss()
else:
loss_g_recon = losses.L1_loss(y, x_gt)
loss_g = w_loss_gan * loss_g_gan + w_loss_recon * loss_g_recon
# ---------------------------------------------------------------------------
# Tensorboard visualizations
# ---------------------------------------------------------------------------
x_in_render = tf.compat.v1.slice(x_in, [0, 0, 0, 0], [-1, -1, -1, 3])
if opts.use_semantic:
x_in_semantic = tf.compat.v1.slice(x_in, [0, 0, 0, 4], [-1, -1, -1, 3])
tb_visualization = tf.compat.v1.concat([x_in_render, x_in_semantic, y, x_gt], axis=2)
else:
tb_visualization = tf.compat.v1.concat([x_in_render, y, x_gt], axis=2)
tf.compat.v1.summary.image('rendered-semantic-generated-gt tuple', tb_visualization)
# Show input appearance images
if opts.use_appearance:
x_app_rgb = tf.compat.v1.slice(x_app, [0, 0, 0, 0], [-1, -1, -1, 3])
x_app_sem = tf.compat.v1.slice(x_app, [0, 0, 0, 7], [-1, -1, -1, 3])
tb_app_visualization = tf.compat.v1.concat([x_app_rgb, x_app_sem], axis=2)
tf.compat.v1.summary.image('input appearance image', tb_app_visualization)
# Loss summaries
with tf.compat.v1.name_scope('Discriminator_Loss'):
tf.compat.v1.summary.scalar('D_real_loss', loss_d_real)
tf.compat.v1.summary.scalar('D_fake_loss', loss_d_fake)
tf.compat.v1.summary.scalar('D_total_loss', loss_d)
with tf.compat.v1.name_scope('Generator_Loss'):
tf.compat.v1.summary.scalar('G_GAN_loss', w_loss_gan * loss_g_gan)
tf.compat.v1.summary.scalar('G_reconstruction_loss', w_loss_recon * loss_g_recon)
tf.compat.v1.summary.scalar('G_total_loss', loss_g)
# ---------------------------------------------------------------------------
# Optimizers
# ---------------------------------------------------------------------------
def get_optimizer(lr, loss, var_list):
optimizer = tf.compat.v1.train.AdamOptimizer(lr, opts.adam_beta1, opts.adam_beta2)
# optimizer = tf.contrib.estimator.TowerOptimizer(optimizer)
return optimizer.minimize(loss, var_list=var_list)
# Training ops.
update_ops = tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.UPDATE_OPS)
with tf.compat.v1.control_dependencies(update_ops):
with tf.compat.v1.variable_scope('optimizers'):
d_vars = utils.model_vars('d_model')[0]
g_vars_all = utils.model_vars('g_model')[0]
train_d = [get_optimizer(opts.d_lr, loss_d, d_vars)]
train_g = [get_optimizer(opts.g_lr, loss_g, g_vars_all)]
train_app_encoder = []
if opts.train_app_encoder:
lr_app = opts.ez_lr
app_enc_vars = utils.model_vars('appearance_net')[0]
train_app_encoder.append(get_optimizer(lr_app, loss_g, app_enc_vars))
ema = tf.compat.v1.train.ExponentialMovingAverage(decay=0.999)
with tf.compat.v1.control_dependencies(train_g + train_app_encoder):
inference_vars_all = g_vars_all
if opts.use_appearance:
app_enc_vars = utils.model_vars('appearance_net')[0]
inference_vars_all += app_enc_vars
ema_op = ema.apply(inference_vars_all)
print('***************************************************')
print('len(g_vars_all) = %d' % len(g_vars_all))
# for ii, v in enumerate(g_vars_all):
# print('%03d) %s' % (ii, str(v)))
print('-------------------------------------------------------')
print('len(d_vars) = %d' % len(d_vars))
# for ii, v in enumerate(d_vars):
# print('%03d) %s' % (ii, str(v)))
if opts.train_app_encoder:
print('-------------------------------------------------------')
print('len(app_enc_vars) = %d' % len(app_enc_vars))
# for ii, v in enumerate(app_enc_vars):
# print('%03d) %s' % (ii, str(v)))
print('***************************************************\n\n')
return {
'train_disc_op': tf.compat.v1.group(train_d),
'train_renderer_op': ema_op,
'total_loss_d': loss_d,
'loss_d_real': loss_d_real,
'loss_d_fake': loss_d_fake,
'loss_g_gan': w_loss_gan * loss_g_gan,
'loss_g_recon': w_loss_recon * loss_g_recon,
'total_loss_g': loss_g}