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trainer_EGSCIT.py
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trainer_EGSCIT.py
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from __future__ import division
import os, pdb, sys
import time
from glob import glob
import tensorflow as tf
import numpy as np
from collections import namedtuple
import scipy.misc
import logging
import math
from utils import *
from loss_func import *
## Use tf record to speed up
from datasets import gta, dataset_utils, celeba
import ops_EGSCIT
from labels_utils import *
import tensorflow_vgg19
class UNIT(object):
def __init__(self, args):
self.model_name = 'UNIT.model'
self.model_dir = args.model_dir
self.test_dir = args.test_dir
self.model_dir = args.model_dir
self.sample_dir = args.sample_dir
self.data_parent_dir = args.data_parent_dir
self.dataset_dir = args.dataset_dir
self.segment_class = args.segment_class
self.scale_num = 4
self.logging = logging
self.logging.basicConfig(filename=os.path.join(args.model_dir, 'INFO.log'),
level=logging.INFO)
self.color_aug = args.color_aug
self.depth_max = args.depth_max
self.epoch = args.epoch # 100000
self.batch_size = args.batch_size # 1
self.img_w = args.img_w
self.img_h = args.img_h
self.img_w_original = args.img_w_original
self.img_h_original = args.img_h_original
self.input_c_dim = args.input_nc
self.output_c_dim = args.output_nc
self.ngf = args.ngf
self.ndf = args.ndf
self.beta1 = args.beta1
# self.lr = args.lr # 0.0001
""" Weight about VAE """
self.KL_weight = args.KL_weight # lambda 1
self.L1_weight = args.L1_weight # lambda 2
""" Weight about VAE Cycle"""
self.KL_cycle_weight = args.KL_cycle_weight # lambda 3
self.L1_cycle_weight = args.L1_cycle_weight # lambda 4
""" Weight about GAN """
self.GAN_weight = args.GAN_weight # lambda 0
""" Weight about VGG loss """
self.GAN_weight = args.GAN_weight # lambda 0
self.style_weight = args.style_weight
self.content_weight = args.content_weight
""" Encoder """
# self.input_c_dim = args.ch # base channel number per layer
self.n_encoder = args.n_encoder
self.n_enc_resblock = args.n_enc_resblock
self.n_enc_share = args.n_enc_share
""" Generator """
self.n_gen_share = args.n_gen_share
self.n_gen_resblock = args.n_gen_resblock
self.n_gen_decoder = args.n_gen_decoder
""" Discriminator """
self.n_dis = args.n_dis # + 2
self.res_dropout = args.res_dropout
self.smoothing = args.smoothing
self.use_lsgan = args.use_lsgan
self.norm = args.norm
self.replay_memory = args.replay_memory
self.pool_size = args.pool_size
# self.img_size = args.img_size
# self.output_c_dim = args.img_ch
# self.augment_flag = args.augment_flag
# self.augment_size = self.img_size + (30 if self.img_size == 256 else 15)
self.normal_weight_init = args.normal_weight_init
self.pretrained_vgg_path = args.pretrained_vgg_path
self.content_loss_IN = args.content_loss_IN
# self.trainA, self.trainB = prepare_data(dataset_name=self.dataset_name, size=self.img_size)
# self.num_batches = max(len(self.trainA) // self.batch_size, len(self.trainB) // self.batch_size)
if args.phase == 'train':
self.is_training = True
self.num_threads = 4
self.capacityCoff = 2
# self.sample_num = 2000
fpath = os.path.join(self.data_parent_dir, self.dataset_dir, 'tf_record_sample_num.txt')
with open(fpath,'r') as f:
self.sample_num = int(f.read().split(':')[1])
else:
self.is_training = False
# during testing to keep the order of the input data
# self.num_threads = 1
# self.capacityCoff = 1
self.num_threads = 4
self.capacityCoff = 2
fpath = os.path.join(self.data_parent_dir, self.dataset_dir, 'tf_record_sample_num.txt')
with open(fpath,'r') as f:
self.sample_num = int(f.read().split(':')[1])
if 'gta' in self.dataset_dir.lower():
self.dataset_tr_obj = gta.get_split(args.phase, os.path.join(self.data_parent_dir, self.dataset_dir),
'gta', self.img_h_original, self.img_w_original, self.segment_class)
elif 'celeba' in self.dataset_dir.lower():
self.dataset_tr_obj = celeba.get_split(args.phase, os.path.join(self.data_parent_dir, self.dataset_dir),
'celeba', self.img_h_original, self.img_w_original, self.segment_class)
elif 'mnist' in self.dataset_dir.lower():
self.dataset_tr_obj = celeba.get_split(args.phase, os.path.join(self.data_parent_dir, self.dataset_dir),
'mnist', self.img_h_original, self.img_w_original, self.segment_class)
# self.dataset_ts_obj = gta.get_split('test', os.path.join(self.data_parent_dir, self.dataset_dir.replace('train','test')),
# 'gta', self.img_h_original, self.img_w_original, self.segment_class)
# elif 'synsf' in self.dataset_dir.lower():
# self.dataset_tr_obj = synsf.get_split('train', os.path.join(self.data_parent_dir, self.dataset_dir),
# 'synsf', self.img_h_original, self.img_w_original, self.segment_class)
# self.dataset_ts_obj = synsf.get_split('test', os.path.join(self.data_parent_dir, self.dataset_dir.replace('train','test')),
# 'synsf', self.img_h_original, self.img_w_original, self.segment_class)
else:
raise Exception(dataset_dir.lower() + ' is not valid')
## Using `ignore_errors()` will drop the element that causes an error.
# self.dataset_tr_obj = self.dataset_tr_obj.apply(tf.data.experimental.ignore_errors())
self.img_name_A, self.img_name_B, self.real_data = self._load_batch_data_tf_noSeg(self.dataset_tr_obj, is_training=self.is_training)
self.real_A = self.real_data[:, :, :, :self.input_c_dim]
self.real_B = self.real_data[:, :, :, self.input_c_dim:self.input_c_dim + self.output_c_dim]
if args.RandInvDomainA:
## Random muliply +-1
shape = self.real_A.get_shape().as_list()
sign = tf.where(tf.random_uniform([shape[0],1,1,1])>0.5, tf.ones([shape[0],1,1,1]), -tf.ones([shape[0],1,1,1]))
sign = tf.tile(sign, [1,shape[1],shape[2],shape[3]])
self.real_A = self.real_A * sign
self.domain_A = self.real_A
self.domain_B = self.real_B
self._build_model()
self.saver = tf.train.Saver(max_to_keep=10)
##############################################################################
# BEGIN of ENCODERS
def encoder(self, x, is_training=True, reuse=False, scope="encoder"):
channel = self.ngf
with tf.variable_scope(scope, reuse=reuse) :
x = ops_EGSCIT.conv(x, channel, kernel=7, stride=1, pad=3, normal_weight_init=self.normal_weight_init, activation_fn='leaky', scope='conv_0')
for i in range(1, self.n_encoder) :
x = ops_EGSCIT.conv(x, channel*2, kernel=3, stride=2, pad=1, normal_weight_init=self.normal_weight_init, activation_fn='leaky', scope='conv_'+str(i))
channel *= 2
# channel = 256
for i in range(0, self.n_enc_resblock) :
x = ops_EGSCIT.resblock(x, channel, kernel=3, stride=1, pad=1, dropout_ratio=self.res_dropout,
normal_weight_init=self.normal_weight_init,
is_training=is_training, norm_fn=self.norm, scope='resblock_'+str(i))
return x
# END of ENCODERS
##############################################################################
##############################################################################
# BEGIN of SHARED LAYERS
# Shared residual-blocks
def share_encoder(self, x, is_training=True, reuse=False, scope="share_encoder"):
channel = self.ngf * pow(2, self.n_encoder-1)
with tf.variable_scope(scope, reuse=reuse) :
for i in range(0, self.n_enc_share) :
x = ops_EGSCIT.resblock(x, channel, kernel=3, stride=1, pad=1, dropout_ratio=self.res_dropout,
normal_weight_init=self.normal_weight_init,
is_training=is_training, norm_fn=self.norm, scope='resblock_'+str(i))
x = ops_EGSCIT.gaussian_noise_layer(x)
return x
def share_generator(self, x, is_training=True, reuse=False, scope="share_generator"):
channel = self.ngf * pow(2, self.n_encoder-1)
with tf.variable_scope(scope, reuse=reuse) :
for i in range(0, self.n_gen_share) :
x = ops_EGSCIT.resblock(x, channel, kernel=3, stride=1, pad=1, dropout_ratio=self.res_dropout,
normal_weight_init=self.normal_weight_init,
is_training=is_training, norm_fn=self.norm, scope='resblock_'+str(i))
return x
# END of SHARED LAYERS
##############################################################################
##############################################################################
# BEGIN of DECODERS
def generator(self, x, is_training=True, reuse=False, scope="generator"):
channel = self.ngf * pow(2, self.n_encoder - 1)
with tf.variable_scope(scope, reuse=reuse) :
for i in range(0, self.n_gen_resblock) :
x = ops_EGSCIT.resblock(x, channel, kernel=3, stride=1, pad=1, dropout_ratio=self.res_dropout,
normal_weight_init=self.normal_weight_init,
is_training=is_training, norm_fn=self.norm, scope='resblock_'+str(i))
for i in range(0, self.n_gen_decoder-1) :
x = ops_EGSCIT.deconv(x, channel//2, kernel=3, stride=2, normal_weight_init=self.normal_weight_init, activation_fn='leaky', scope='deconv_'+str(i))
channel = channel // 2
x = ops_EGSCIT.deconv(x, self.output_c_dim, kernel=1, stride=1, normal_weight_init=self.normal_weight_init, activation_fn='tanh', scope='deconv_tanh')
# x = ops_EGSCIT.deconv(x, self.output_c_dim, kernel=1, stride=1, normal_weight_init=self.normal_weight_init, activation_fn=None, scope='deconv_out')
return x
# END of DECODERS
##############################################################################
##############################################################################
# BEGIN of DISCRIMINATORS
def discriminator(self, x, reuse=False, is_training=True, scope="discriminator", activation_fn=ops_EGSCIT.LeakyReLU):
channel = self.ndf
with tf.variable_scope(scope, reuse=reuse):
x = activation_fn(slim.conv2d(x, channel, 4, 2, activation_fn=activation_fn, scope='conv_0'))
for i in range(1, self.n_dis) :
channel *= 2
x = activation_fn(ops_EGSCIT.instance_norm(slim.conv2d(x, channel, 4, 2, activation_fn=activation_fn, scope='conv_'+str(i)),scope='ins_norm_'+str(i)))
x = activation_fn(slim.conv2d(x, 1, 1, 1, activation_fn=None, scope='dis_logit'))
return x
# END of DISCRIMINATORS
##############################################################################
def translation(self, x_A, x_B):
out = tf.concat([self.encoder(x_A, self.is_training, scope="encoder_A"), self.encoder(x_B, self.is_training, scope="encoder_B")], axis=0)
shared = self.share_encoder(out, self.is_training)
out = self.share_generator(shared, self.is_training)
out_A = self.generator(out, self.is_training, scope="generator_A")
out_B = self.generator(out, self.is_training, scope="generator_B")
x_Aa, x_Ba = tf.split(out_A, 2, axis=0)
x_Ab, x_Bb = tf.split(out_B, 2, axis=0)
return x_Aa, x_Ba, x_Ab, x_Bb, shared
def generate_a2b(self, x_A):
out = self.encoder(x_A, self.is_training, reuse=True, scope="encoder_A")
shared = self.share_encoder(out, self.is_training, reuse=True)
out = self.share_generator(shared, self.is_training, reuse=True)
out = self.generator(out, self.is_training, reuse=True, scope="generator_B")
return out, shared
def generate_b2a(self, x_B):
out = self.encoder(x_B, self.is_training, reuse=True, scope="encoder_B")
shared = self.share_encoder(out, self.is_training, reuse=True)
out = self.share_generator(shared, self.is_training, reuse=True)
out = self.generator(out, self.is_training, reuse=True, scope="generator_A")
return out, shared
def _define_input(self):
## Input param
self.is_training = tf.placeholder(tf.bool, None, name='is_training')
self.lr = tf.placeholder(tf.float32, None, name='learning_rate')
def _build_model(self):
self._define_input()
# self.is_training = tf.placeholder(tf.bool)
domain_A = self.domain_A
domain_B = self.domain_B
with tf.variable_scope('UNIT'):
""" Define Encoder, Generator, Discriminator """
x_aa, x_ba, x_ab, x_bb, shared = self.translation(domain_A, domain_B)
x_bab, shared_bab = self.generate_a2b(x_ba)
x_aba, shared_aba = self.generate_b2a(x_ab)
self.x_aa, self.x_ba, self.x_ab, self.x_bb = x_aa, x_ba, x_ab, x_bb
# real_A_logit, real_B_logit = self.discriminate_real(domain_A, domain_B)
real_A_logit = self.discriminator(domain_A, scope="discriminator_A")
real_B_logit = self.discriminator(domain_B, scope="discriminator_B")
if self.replay_memory :
self.fake_A_pool = ImagePool_UNIT(self.pool_size) # pool of generated A
self.fake_B_pool = ImagePool_UNIT(self.pool_size) # pool of generated B
# fake_A_logit, fake_B_logit = self.discriminate_fake_pool(x_ba, x_ab)
fake_A_logit = self.discriminator(self.fake_A_pool.query(x_ba), reuse=True, scope="discriminator_A") # replay memory
fake_B_logit = self.discriminator(self.fake_B_pool.query(x_ab), reuse=True, scope="discriminator_B") # replay memory
else :
# fake_A_logit, fake_B_logit = self.discriminate_fake(x_ba, x_ab)
fake_A_logit = self.discriminator(x_ba, reuse=True, scope="discriminator_A")
fake_B_logit = self.discriminator(x_ab, reuse=True, scope="discriminator_B")
""" Generated Image """
self.fake_B, _ = self.generate_a2b(domain_A) # for test
self.fake_A, _ = self.generate_b2a(domain_B) # for test
""" Define Loss """
G_ad_loss_a = ops_EGSCIT.generator_loss(fake_A_logit, smoothing=self.smoothing, use_lsgan=self.use_lsgan)
G_ad_loss_b = ops_EGSCIT.generator_loss(fake_B_logit, smoothing=self.smoothing, use_lsgan=self.use_lsgan)
D_ad_loss_a = ops_EGSCIT.discriminator_loss(real_A_logit, fake_A_logit, smoothing=self.smoothing, use_lasgan=self.use_lsgan)
D_ad_loss_b = ops_EGSCIT.discriminator_loss(real_B_logit, fake_B_logit, smoothing=self.smoothing, use_lasgan=self.use_lsgan)
enc_loss = ops_EGSCIT.KL_divergence(shared)
enc_bab_loss = ops_EGSCIT.KL_divergence(shared_bab)
enc_aba_loss = ops_EGSCIT.KL_divergence(shared_aba)
l1_loss_a = ops_EGSCIT.L1_loss(x_aa, domain_A) # identity
l1_loss_b = ops_EGSCIT.L1_loss(x_bb, domain_B) # identity
l1_loss_aba = ops_EGSCIT.L1_loss(x_aba, domain_A) # reconstruction
l1_loss_bab = ops_EGSCIT.L1_loss(x_bab, domain_B) # reconstruction
Generator_A_loss = self.GAN_weight * G_ad_loss_a + \
self.L1_weight * l1_loss_a + \
self.L1_cycle_weight * l1_loss_aba + \
self.KL_weight * enc_loss + \
self.KL_cycle_weight * enc_bab_loss
Generator_B_loss = self.GAN_weight * G_ad_loss_b + \
self.L1_weight * l1_loss_b + \
self.L1_cycle_weight * l1_loss_bab + \
self.KL_weight * enc_loss + \
self.KL_cycle_weight * enc_aba_loss
Discriminator_A_loss = self.GAN_weight * D_ad_loss_a
Discriminator_B_loss = self.GAN_weight * D_ad_loss_b
self.Generator_loss = Generator_A_loss + Generator_B_loss
self.Discriminator_loss = Discriminator_A_loss + Discriminator_B_loss
self.D_loss_A = Discriminator_A_loss
self.D_loss_B = Discriminator_B_loss
""" Training """
t_vars = tf.trainable_variables()
G_vars = [var for var in t_vars if ('generator' in var.name) or ('encoder' in var.name)]
D_vars = [var for var in t_vars if 'discriminator' in var.name]
# with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
# pdb.set_trace()
self.G_optim = tf.train.AdamOptimizer(self.lr, beta1=0.5, beta2=0.999).minimize(self.Generator_loss, var_list=G_vars)
self.D_optim = tf.train.AdamOptimizer(self.lr, beta1=0.5, beta2=0.999).minimize(self.Discriminator_loss, var_list=D_vars)
"""" Summary """
self.G_ad_loss_a_sum = tf.summary.scalar("G_ad_loss_a", G_ad_loss_a)
self.G_ad_loss_b_sum = tf.summary.scalar("G_ad_loss_b", G_ad_loss_b)
self.l1_loss_a_sum = tf.summary.scalar("l1_loss_a", l1_loss_a)
self.l1_loss_b_sum = tf.summary.scalar("l1_loss_b", l1_loss_b)
self.l1_loss_aba_sum = tf.summary.scalar("l1_loss_aba", l1_loss_aba)
self.l1_loss_bab_sum = tf.summary.scalar("l1_loss_bab", l1_loss_bab)
self.enc_loss_sum = tf.summary.scalar("KL_enc_loss", enc_loss)
self.enc_bab_loss_sum = tf.summary.scalar("KL_enc_bab_loss", enc_bab_loss)
self.enc_aba_loss_sum = tf.summary.scalar("KL_enc_aba_loss", enc_aba_loss)
self.all_G_loss = tf.summary.scalar("Generator_loss", self.Generator_loss)
self.all_D_loss = tf.summary.scalar("Discriminator_loss", self.Discriminator_loss)
self.G_A_loss = tf.summary.scalar("G_A_loss", Generator_A_loss)
self.G_B_loss = tf.summary.scalar("G_B_loss", Generator_B_loss)
self.D_A_loss = tf.summary.scalar("D_A_loss", Discriminator_A_loss)
self.D_B_loss = tf.summary.scalar("D_B_loss", Discriminator_B_loss)
self.G_loss = tf.summary.merge([self.G_A_loss, self.G_B_loss, self.all_G_loss,
self.G_ad_loss_a_sum, self.G_ad_loss_b_sum, self.l1_loss_a_sum, self.l1_loss_b_sum,
self.l1_loss_aba_sum, self.l1_loss_bab_sum, self.enc_loss_sum,
self.enc_bab_loss_sum, self.enc_aba_loss_sum])
self.D_loss = tf.summary.merge([self.D_A_loss, self.D_B_loss, self.all_D_loss])
self.init_op = tf.global_variables_initializer()
def init_net(self, args):
# if args.pretrained_path is not None:
# var = tf.get_collection(tf.GraphKeys.VARIABLES, scope='LinkNet')
# var = var_filter_by_exclude(var, exclude_scopes=['Adam'])
# self.saverPart = tf.train.Saver(var, max_to_keep=5)
if args.pretrained_vgg_path is not None:
var = tf.get_collection(tf.GraphKeys.VARIABLES, scope='vgg_19')
var = var_filter_by_exclude(var, exclude_scopes=['Adam'])
self.saverVggPart = tf.train.Saver(var, max_to_keep=5)
if args.test_model_path is not None:
var = tf.get_collection(tf.GraphKeys.VARIABLES, scope='UNIT')
var = var_filter_by_exclude(var, exclude_scopes=['Adam'])
var = var_filter_by_exclude(var, exclude_scopes=['UNIT/discriminator'])
self.saverTest = tf.train.Saver(var, max_to_keep=5)
self.summary_writer = tf.summary.FileWriter(args.model_dir)
sv = tf.train.Supervisor(logdir=args.model_dir, is_chief=True, saver=None, summary_op=None,
summary_writer=self.summary_writer, save_model_secs=0, ready_for_local_init_op=None)
if args.phase == 'train':
gpu_options = tf.GPUOptions(allow_growth=False)
else:
gpu_options = tf.GPUOptions(allow_growth=False)
sess_config = tf.ConfigProto(allow_soft_placement=True, gpu_options=gpu_options)
self.sess = sv.prepare_or_wait_for_session(config=sess_config)
# if args.pretrained_path is not None:
# self.saverPart.restore(self.sess, args.pretrained_path)
# print('restored from pretrained_path:', args.pretrained_path)
# elif self.ckpt_path is not None:
# self.saver.restore(self.sess, self.ckpt_path)
# print('restored from ckpt_path:', self.ckpt_path)
def train(self, args):
"""Train SG-GAN"""
self.sess.run(self.init_op)
self.writer = tf.summary.FileWriter(args.model_dir, self.sess.graph)
if args.pretrained_vgg_path is not None:
self.saverVggPart.restore(self.sess, args.pretrained_vgg_path)
print('restored from pretrained_vgg_path:', args.pretrained_vgg_path)
# if args.pretrained_path is not None:
# self.saverPart.restore(self.sess, args.pretrained_path)
# print('restored from pretrained_path:', args.pretrained_path)
counter = args.global_step
start_time = time.time()
if 0==args.global_step:
if args.continue_train and self.load_last_ckpt(args.model_dir):
print(" [*] load_last_ckpt SUCCESS")
else:
print(" [!] load_last_ckpt Failed...")
else:
## global_step is set manually
if args.continue_train and self.load_ckpt(args.model_dir, args.global_step):
print(" [*] load_ckpt SUCCESS")
else:
print(" [!] load_ckpt Failed...")
for epoch in range(args.epoch):
# batch_idxs = self.sample_num // self.batch_size
batch_idxs = self.sample_num
# lr = args.lr if epoch < args.epoch_step else args.lr*(args.epoch-epoch)/(args.epoch-args.epoch_step)
# idx = int(epoch/len(self.weights_schedule))
# loss_weights = self.weights_schedule[idx]
for idx in range(0, batch_idxs):
step_ph = epoch*batch_idxs + idx
num_steps = args.epoch*batch_idxs
lr = args.lr*((1 - counter / num_steps)**0.9)
# Update D
_, d_loss, summary_str = self.sess.run([self.D_optim, self.Discriminator_loss, self.D_loss], \
feed_dict={self.is_training : True, self.lr : lr})
self.writer.add_summary(summary_str, counter)
# Update G
for g_iter in range(args.G_update):
fake_A, fake_B, _, g_loss, summary_str = self.sess.run([self.fake_A, self.fake_B, self.G_optim, self.Generator_loss, self.G_loss], \
feed_dict={self.is_training : True, self.lr : lr})
self.writer.add_summary(summary_str, counter)
counter += 1
if np.mod(counter, args.print_freq) == 1:
# display training status
print("Epoch: [%2d] [%4d/%4d] time: %4.4f d_loss: %.8f, g_loss: %.8f" \
% (epoch, idx, batch_idxs, time.time() - start_time, d_loss, g_loss))
self.sample_model(args.sample_dir, epoch, counter)
if (counter>3000 and np.mod(counter, args.save_freq) == 2) or (idx==batch_idxs-1):
self.save(args.model_dir, counter)
def sample_model(self, sample_dir, epoch, idx):
img_name_A, img_name_B, real_A, real_B, fake_A, fake_B, x_aa, x_ba, x_ab, x_bb = self.sess.run(
[self.img_name_A, self.img_name_B, self.real_A, self.real_B, self.fake_A, self.fake_B, self.x_aa, self.x_ba, self.x_ab, self.x_bb], \
feed_dict={self.is_training : False}
)
real_A = unprocess_image(real_A, 127.5, 127.5)
real_B = unprocess_image(real_B, 127.5, 127.5)
fake_A = unprocess_image(fake_A, 127.5, 127.5)
fake_B = unprocess_image(fake_B, 127.5, 127.5)
x_aa = unprocess_image(x_aa, 127.5, 127.5)
x_ba = unprocess_image(x_ba, 127.5, 127.5)
x_ab = unprocess_image(x_ab, 127.5, 127.5)
x_bb = unprocess_image(x_bb, 127.5, 127.5)
img_name_A = img_name_A[0]
img_name_B = img_name_B[0]
pdb.set_trace()
save_images(real_A, [self.batch_size, 1],
'{}/real_A_{:02d}_{:04d}_{}.png'.format(sample_dir, epoch, idx, img_name_B.decode("utf-8").split(".")[0]))
save_images(real_B, [self.batch_size, 1],
'{}/real_B_{:02d}_{:04d}_{}.png'.format(sample_dir, epoch, idx, img_name_A.decode("utf-8").split(".")[0]))
save_images(fake_A, [self.batch_size, 1],
'{}/fake_A_{:02d}_{:04d}_{}.png'.format(sample_dir, epoch, idx, img_name_B.decode("utf-8").split(".")[0]))
save_images(fake_B, [self.batch_size, 1],
'{}/fake_B_{:02d}_{:04d}_{}.png'.format(sample_dir, epoch, idx, img_name_A.decode("utf-8").split(".")[0]))
save_images(x_aa, [self.batch_size, 1],
'{}/x_aa_{:02d}_{:04d}_{}.png'.format(sample_dir, epoch, idx, img_name_B.decode("utf-8").split(".")[0]))
save_images(x_ba, [self.batch_size, 1],
'{}/x_ba_{:02d}_{:04d}_{}.png'.format(sample_dir, epoch, idx, img_name_A.decode("utf-8").split(".")[0]))
save_images(x_ab, [self.batch_size, 1],
'{}/x_ab_{:02d}_{:04d}_{}.png'.format(sample_dir, epoch, idx, img_name_B.decode("utf-8").split(".")[0]))
save_images(x_bb, [self.batch_size, 1],
'{}/x_bb_{:02d}_{:04d}_{}.png'.format(sample_dir, epoch, idx, img_name_A.decode("utf-8").split(".")[0]))
def save(self, model_dir, step):
if not os.path.exists(model_dir):
os.makedirs(model_dir)
self.saver.save(self.sess,
os.path.join(model_dir, self.model_name),
global_step=step)
def load_last_ckpt(self, model_dir):
print(" [*] Reading last checkpoint...")
ckpt = tf.train.get_checkpoint_state(model_dir)
if ckpt and ckpt.model_checkpoint_path:
ckpt_name = os.path.basename(ckpt.model_checkpoint_path)
self.saver.restore(self.sess, os.path.join(model_dir, ckpt_name))
return True
else:
return False
def load_ckpt(self, model_dir, step):
print(" [*] Reading checkpoint of step {}...".format(step))
# ckpt_path = glob(os.path.join(model_dir, '*model-{}*'.format(step)))
ckpt_path = os.path.join(model_dir, '{}-{}'.format(self.model_name, step))
ckpt_path_backup = os.path.join(model_dir, 'backup_{}-{}'.format(self.model_name, step))
if os.path.exists(ckpt_path+'.meta'):
self.saver.restore(self.sess, ckpt_path)
return True
elif os.path.exists(ckpt_path_backup+'.meta'):
self.saver.restore(self.sess, ckpt_path_backup)
return True
else:
return False
def test(self, args):
"""Train SG-GAN"""
self.sess.run(self.init_op)
if args.pretrained_path is not None:
self.saverPart.restore(self.sess, args.pretrained_path)
print('restored from pretrained_path:', args.pretrained_path)
if args.test_model_path is not None:
self.saverTest.restore(self.sess, args.test_model_path)
print('restored from test_model_path:', args.test_model_path)
A_dir = os.path.join(args.test_dir+'_'+args.dataset_dir.split('/')[-1]+'_%dstyle_TrAsTs'%args.num_style, 'A')
B_dir = os.path.join(args.test_dir+'_'+args.dataset_dir.split('/')[-1]+'_%dstyle_TrAsTs'%args.num_style, 'B')
A2B_dir = os.path.join(args.test_dir+'_'+args.dataset_dir.split('/')[-1]+'_%dstyle_TrAsTs'%args.num_style, 'ab')
B2A_dir = os.path.join(args.test_dir+'_'+args.dataset_dir.split('/')[-1]+'_%dstyle_TrAsTs'%args.num_style, 'ba')
if not os.path.exists(A2B_dir):
os.makedirs(A2B_dir)
if not os.path.exists(B2A_dir):
os.makedirs(B2A_dir)
if not os.path.exists(A_dir):
os.makedirs(A_dir)
if not os.path.exists(B_dir):
os.makedirs(B_dir)
counter = 0
start_time = time.time()
# if self.load_ckpt(args.model_dir, args.global_step):
# print " [*] Load SUCCESS, args.global_step:%d"%args.global_step
# else:
# raise Exception(" [!] Load failed...")
if args.num_style>1:
assert args.batch_size == 1
batch_idxs = 1
for idx in range(batch_idxs):
counter += 1
# self.sample_model(args.test_dir, epoch, idx)
self.get_test_result_multi_style(A_dir, B_dir, A2B_dir, B2A_dir, args.num_style, args.save_test_dis_score)
if np.mod(counter, args.print_freq) == 1:
# display training status
print("Epoch: [%2d] [%4d/%4d] time: %4.4f" \
% (1, idx, batch_idxs, time.time() - start_time))
else:
for epoch in range(2):
# pdb.set_trace()
batch_idxs = int(math.ceil(self.sample_num/self.batch_size))
for idx in range(0, batch_idxs):
counter += 1
# self.sample_model(args.test_dir, epoch, idx)
self.get_test_result(A_dir, B_dir, A2B_dir, B2A_dir, args.save_test_dis_score)
if np.mod(counter, args.print_freq) == 1:
# display training status
print("Epoch: [%2d] [%4d/%4d] time: %4.4f" \
% (epoch, idx, batch_idxs, time.time() - start_time))
def get_test_result_multi_style(self, A_dir, B_dir, A2B_dir, B2A_dir, num_style, save_dis_score=False):
img_name_A, img_name_B, real_A, real_B = [], [], [], []
for i in range(num_style):
img_name_A_one, img_name_B_one, real_A_one, real_B_one = self.sess.run(
[self.img_name_A, self.img_name_B, self.real_A, self.real_B], \
feed_dict={self.is_training : False}
)
img_name_A.append(img_name_A_one)
img_name_B.append(img_name_B_one)
real_A.append(real_A_one)
real_B.append(real_B_one)
combined = list(zip(img_name_A, img_name_B, real_A, real_B))
combined = sorted(combined, key=lambda x: x[0])
img_name_A[:], img_name_B[:], real_A[:], real_B[:] = zip(*combined)
img_name_A = np.concatenate(img_name_A, axis=0)
img_name_B = np.concatenate(img_name_B, axis=0)
# pdb.set_trace()
real_A = np.concatenate(real_A, axis=0)
real_B = np.concatenate(real_B, axis=0)
for i in range(num_style):
for j in range(num_style):
x_ba_tmp, x_ab_tmp = self.sess.run(
[self.x_ba, self.x_ab], \
feed_dict={self.is_training : True,
self.real_A : np.tile(real_A[i,:,:,:], [1, 1, 1, 1]),
self.real_B : np.tile(real_B[j,:,:,:], [1, 1, 1, 1])}
# feed_dict={self.is_training : False,
# self.real_A : np.tile(real_A[i,:,:,:], [1, 1, 1, 1]),
# self.real_B : np.tile(real_B[j,:,:,:], [1, 1, 1, 1])}
)
x_ba_tmp_img = unprocess_image(x_ba_tmp, 127.5, 127.5)
x_ab_tmp_img = unprocess_image(x_ab_tmp, 127.5, 127.5)
if save_dis_score:
scipy.misc.imsave('{}/{}_to_{}_{:04f}.png'.format(B2A_dir, img_name_B[j].split(".")[0], img_name_A[i].decode("utf-8").split(".")[0], d_loss_a), x_ba_tmp_img[0])
scipy.misc.imsave('{}/{}_to_{}_{:04f}.png'.format(A2B_dir, img_name_A[i].decode("utf-8").split(".")[0], img_name_B[j].split(".")[0], d_loss_b), x_ab_tmp_img[0])
else:
scipy.misc.imsave('{}/{}_to_{}.png'.format(B2A_dir, img_name_B[j].split(".")[0], img_name_A[i].decode("utf-8").split(".")[0]), x_ba_tmp_img[0])
scipy.misc.imsave('{}/{}_to_{}.png'.format(A2B_dir, img_name_A[i].decode("utf-8").split(".")[0], img_name_B[j].split(".")[0]), x_ab_tmp_img[0])
real_A = unprocess_image(real_A, 127.5, 127.5)
real_B = unprocess_image(real_B, 127.5, 127.5)
for i in range(img_name_A.shape[0]):
scipy.misc.imsave('{}/{}.png'.format(A_dir, img_name_A[i].decode("utf-8").split(".")[0]), real_A[i,:,:,:])
scipy.misc.imsave('{}/{}.png'.format(B_dir, img_name_B[i].decode("utf-8").split(".")[0]), real_B[i,:,:,:])
def get_test_result(self, A_dir, B_dir, A2B_dir, B2A_dir, save_dis_score=False):
img_name_A, img_name_B, real_A, real_B, x_ba, x_ab, d_loss_a, d_loss_b = self.sess.run(
[self.img_name_A, self.img_name_B, self.real_A, self.real_B, self.x_ba, self.x_ab, self.D_loss_A, self.D_loss_B], \
feed_dict={self.is_training : True}
# feed_dict={self.is_training : False}
)
real_A = unprocess_image(real_A, 127.5, 127.5)
real_B = unprocess_image(real_B, 127.5, 127.5)
x_ba = unprocess_image(x_ba, 127.5, 127.5)
x_ab = unprocess_image(x_ab, 127.5, 127.5)
for i in range(img_name_A.shape[0]):
scipy.misc.imsave('{}/{}.png'.format(A_dir, img_name_A[i].decode("utf-8").split(".")[0]), real_A[i,:,:,:])
scipy.misc.imsave('{}/{}.png'.format(B_dir, img_name_B[i].decode("utf-8").split(".")[0]), real_B[i,:,:,:])
if save_dis_score:
scipy.misc.imsave('{}/{}_to_{}_{:04f}.png'.format(B2A_dir, img_name_B[i].decode("utf-8").split(".")[0], img_name_A[i].decode("utf-8").split(".")[0], d_loss_a), x_ba[i,:,:,:])
scipy.misc.imsave('{}/{}_to_{}_{:04f}.png'.format(A2B_dir, img_name_A[i].decode("utf-8").split(".")[0], img_name_B[i].decode("utf-8").split(".")[0], d_loss_b), x_ab[i,:,:,:])
else:
scipy.misc.imsave('{}/{}_to_{}.png'.format(B2A_dir, img_name_B[i].decode("utf-8").split(".")[0], img_name_A[i].decode("utf-8").split(".")[0]), x_ba[i,:,:,:])
scipy.misc.imsave('{}/{}_to_{}.png'.format(A2B_dir, img_name_A[i].decode("utf-8").split(".")[0], img_name_B[i].decode("utf-8").split(".")[0]), x_ab[i,:,:,:])
def _load_batch_data_tf(self, dataset, is_training=True):
data_provider = slim.dataset_data_provider.DatasetDataProvider(dataset, common_queue_capacity=32, common_queue_min=8)
img_name_A, img_name_B, img_A, img_B, img_A_seg, img_B_seg, img_A_seg_class, img_B_seg_class, A_seg_valid, B_seg_valid = data_provider.get([
'image_name_A', 'image_name_B', 'image_raw_A', 'image_raw_B', 'image_raw_A_seg', 'image_raw_B_seg', 'image_raw_A_seg_class', 'image_raw_B_seg_class', 'A_seg_valid', 'B_seg_valid'])
img_A = tf.reshape(img_A, [self.img_h_original, self.img_w_original, 3])
img_B = tf.reshape(img_B, [self.img_h_original, self.img_w_original, 3])
img_A_seg = tf.reshape(img_A_seg, [self.img_h_original, self.img_w_original, 3])
img_B_seg = tf.reshape(img_B_seg, [self.img_h_original, self.img_w_original, 3])
# img_A_seg_class = tf.reshape(img_A_seg_class, [int(self.img_h_original/8), int(self.img_w_original/8), self.segment_class])
# img_B_seg_class = tf.reshape(img_B_seg_class, [int(self.img_h_original/8), int(self.img_w_original/8), self.segment_class])
# img_A_seg_class = tf.to_float(img_A_seg_class)
# img_B_seg_class = tf.to_float(img_B_seg_class)
img_A_seg_class = tf.reshape(img_A_seg_class, [self.img_h_original, self.img_w_original, 1]) ## [batch, h, w]
img_B_seg_class = tf.reshape(img_B_seg_class, [self.img_h_original, self.img_w_original, 1])
## Perform one-hot-encoding on the ground truth annotation to get same shape as the logits
# img_A_seg_class = tf.one_hot(img_A_seg_class, self.segment_class, axis=-1) ## [batch, h, w, class]
# img_B_seg_class = tf.one_hot(img_B_seg_class, self.segment_class, axis=-1)
img_A = process_image(tf.to_float(img_A), 127.5, 127.5)
img_B = process_image(tf.to_float(img_B), 127.5, 127.5)
img_A_seg = process_image(tf.to_float(img_A_seg), 127.5, 127.5)
img_B_seg = process_image(tf.to_float(img_B_seg), 127.5, 127.5)
imgs_name_A, imgs_name_B, imgs_A, imgs_B, imgs_A_seg, imgs_B_seg, imgs_A_seg_class, imgs_B_seg_class, A_seg_valids, B_seg_valids = tf.train.batch(
[img_name_A, img_name_B, img_A, img_B, img_A_seg, img_A_seg, img_A_seg_class, img_B_seg_class, A_seg_valid, B_seg_valid],
batch_size=self.batch_size, num_threads=self.num_threads, capacity=self.capacityCoff * self.batch_size)
if is_training:
color_prob=0.5 if self.color_aug else 0.0
imgs_A, imgs_A_seg, imgs_A_seg_class, imgs_A_depth, _ = data_augment(
rgb=imgs_A, # 3 channels
seg=imgs_A_seg, # 3 channels
seg_class_map=imgs_A_seg_class, # seg_class channels
resize=[self.img_h, self.img_w], # (width, height) tuple or None
horizontal_flip=True,
crop_probability=0.5, # How often we do crops
color_probability=color_prob) # How often we do color jitter
imgs_B, imgs_B_seg, imgs_B_seg_class, imgs_B_depth, _ = data_augment(
rgb=imgs_B, # 3 channels
seg=imgs_B_seg, # 3 channels
seg_class_map=imgs_B_seg_class, # seg_class channels
resize=[self.img_h, self.img_w], # (width, height) tuple or None
horizontal_flip=True,
crop_probability=0.5) # How often we do crops
# color_probability=color_prob) # How often we do color jitter
else:
imgs_A, imgs_A_seg, imgs_A_seg_class, imgs_A_depth, _ = data_augment(
rgb=imgs_A, # 3 channels
seg=imgs_A_seg, # 3 channels
seg_class_map=imgs_A_seg_class, # 1 channel
resize=[self.img_h, self.img_w]) # (width, height) tuple or None
imgs_B, imgs_B_seg, imgs_B_seg_class, imgs_B_depth, _ = data_augment(
rgb=imgs_B, # 3 channels
seg=imgs_B_seg, # 3 channels
seg_class_map=imgs_B_seg_class, # 1 channel
resize=[self.img_h, self.img_w]) # (width, height) tuple or None
imgs_AB = tf.concat([imgs_A, imgs_B], axis=-1)
imgs_AB_seg = tf.concat([imgs_A_seg, imgs_B_seg], axis=-1)
imgs_A_seg_class = tf.squeeze(imgs_A_seg_class, axis=-1)
imgs_B_seg_class = tf.squeeze(imgs_B_seg_class, axis=-1)
return imgs_name_A, imgs_name_B, imgs_AB, imgs_AB_seg, imgs_A_seg_class, imgs_B_seg_class, A_seg_valids, B_seg_valids
def _load_batch_data_tf_noSeg(self, dataset, is_training=True):
data_provider = slim.dataset_data_provider.DatasetDataProvider(dataset, common_queue_capacity=32, common_queue_min=8)
img_name_A, img_name_B, img_A, img_B = data_provider.get([
'image_name_A', 'image_name_B', 'image_raw_A', 'image_raw_B'])
img_A = tf.reshape(img_A, [self.img_h_original, self.img_w_original, 3])
img_B = tf.reshape(img_B, [self.img_h_original, self.img_w_original, 3])
img_A = process_image(tf.to_float(img_A), 127.5, 127.5)
img_B = process_image(tf.to_float(img_B), 127.5, 127.5)
imgs_name_A, imgs_name_B, imgs_A, imgs_B = tf.train.batch(
[img_name_A, img_name_B, img_A, img_B],
batch_size=self.batch_size, num_threads=self.num_threads, capacity=self.capacityCoff * self.batch_size)
if is_training:
color_prob=0.5 if self.color_aug else 0.0
crop_probability = 0.5
# horizontal_flip = False
horizontal_flip = True
imgs_A, _, _, _, _ = data_augment(
rgb=imgs_A, # 3 channels
resize=[self.img_h, self.img_w], # (width, height) tuple or None
horizontal_flip=horizontal_flip,
crop_probability=crop_probability, # How often we do crops
color_probability=color_prob) # How often we do color jitter
imgs_B, _, _, _, _ = data_augment(
rgb=imgs_B, # 3 channels
resize=[self.img_h, self.img_w], # (width, height) tuple or None
horizontal_flip=horizontal_flip,
crop_probability=crop_probability) # How often we do crops
# color_probability=color_prob) # How often we do color jitter
else:
imgs_A, _, _, _, _ = data_augment(
rgb=imgs_A, # 3 channels
resize=[self.img_h, self.img_w]) # (width, height) tuple or None
imgs_B, _, _, _, _ = data_augment(
rgb=imgs_B, # 3 channels
resize=[self.img_h, self.img_w]) # (width, height) tuple or None
imgs_AB = tf.concat([imgs_A, imgs_B], axis=-1)
return imgs_name_A, imgs_name_B, imgs_AB
class UNIT_VAEGAN_recon(UNIT):
def VAEGAN_recon(self, x, is_training, scope='VAEGAN_recon', reuse=None):
with tf.variable_scope(scope, reuse=reuse):
enc = self.encoder(x, is_training, scope="part1")
latent = self.share_encoder(enc, is_training, scope="part2")
out = self.share_generator(latent, is_training, scope="part3")
out = self.generator(out, is_training, scope="part4")
return out, latent
def _build_model(self):
self._define_input()
# self.is_training = tf.placeholder(tf.bool)
domain_A = self.domain_A
domain_B = self.domain_B
self.x_aa, latent_A = self.VAEGAN_recon(domain_A, self.is_training, scope='VAEGAN_recon_A')
self.x_bb, latent_B = self.VAEGAN_recon(domain_B, self.is_training, scope='VAEGAN_recon_B')
real_A_logit = self.discriminator(domain_A, scope="discriminator_A")
real_B_logit = self.discriminator(domain_B, scope="discriminator_B")
fake_A_logit = self.discriminator(self.x_aa, reuse=True, scope="discriminator_A")
fake_B_logit = self.discriminator(self.x_bb, reuse=True, scope="discriminator_B")
""" Define Loss """
G_ad_loss_a = ops_EGSCIT.generator_loss(fake_A_logit, smoothing=self.smoothing, use_lsgan=self.use_lsgan)
G_ad_loss_b = ops_EGSCIT.generator_loss(fake_B_logit, smoothing=self.smoothing, use_lsgan=self.use_lsgan)
D_ad_loss_a = ops_EGSCIT.discriminator_loss(real_A_logit, fake_A_logit, smoothing=self.smoothing, use_lasgan=self.use_lsgan)
D_ad_loss_b = ops_EGSCIT.discriminator_loss(real_B_logit, fake_B_logit, smoothing=self.smoothing, use_lasgan=self.use_lsgan)
enc_loss_a = ops_EGSCIT.KL_divergence(latent_A)
enc_loss_b = ops_EGSCIT.KL_divergence(latent_B)
l1_loss_a = ops_EGSCIT.L1_loss(self.x_aa, domain_A) # identity
l1_loss_b = ops_EGSCIT.L1_loss(self.x_bb, domain_B) # identity
Generator_A_loss = self.GAN_weight * G_ad_loss_a + \
self.L1_weight * l1_loss_a + \
self.KL_weight * enc_loss_a
Generator_B_loss = self.GAN_weight * G_ad_loss_b + \
self.L1_weight * l1_loss_b + \
self.KL_weight * enc_loss_b
Discriminator_A_loss = self.GAN_weight * D_ad_loss_a
Discriminator_B_loss = self.GAN_weight * D_ad_loss_b
self.Generator_loss = Generator_A_loss + Generator_B_loss
self.Discriminator_loss = Discriminator_A_loss + Discriminator_B_loss
self.D_loss_A = Discriminator_A_loss
self.D_loss_B = Discriminator_B_loss
""" Training """
t_vars = tf.trainable_variables()
G_vars = [var for var in t_vars if 'VAEGAN_recon' in var.name]
D_vars = [var for var in t_vars if 'discriminator' in var.name]
# with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
# pdb.set_trace()
self.G_optim = tf.train.AdamOptimizer(self.lr, beta1=0.5, beta2=0.999).minimize(self.Generator_loss, var_list=G_vars)
self.D_optim = tf.train.AdamOptimizer(self.lr, beta1=0.5, beta2=0.999).minimize(self.Discriminator_loss, var_list=D_vars)
"""" Summary """
self.G_ad_loss_a_sum = tf.summary.scalar("G_ad_loss_a", G_ad_loss_a)
self.G_ad_loss_b_sum = tf.summary.scalar("G_ad_loss_b", G_ad_loss_b)
self.l1_loss_a_sum = tf.summary.scalar("l1_loss_a", l1_loss_a)
self.l1_loss_b_sum = tf.summary.scalar("l1_loss_b", l1_loss_b)
self.enc_loss_a_sum = tf.summary.scalar("KL_enc_loss_a", enc_loss_a)
self.enc_loss_b_sum = tf.summary.scalar("KL_enc_loss_b", enc_loss_b)
self.all_G_loss = tf.summary.scalar("Generator_loss", self.Generator_loss)
self.all_D_loss = tf.summary.scalar("Discriminator_loss", self.Discriminator_loss)
self.G_A_loss = tf.summary.scalar("G_A_loss", Generator_A_loss)
self.G_B_loss = tf.summary.scalar("G_B_loss", Generator_B_loss)
self.D_A_loss = tf.summary.scalar("D_A_loss", Discriminator_A_loss)
self.D_B_loss = tf.summary.scalar("D_B_loss", Discriminator_B_loss)
self.G_loss = tf.summary.merge([self.G_A_loss, self.G_B_loss, self.all_G_loss,
self.G_ad_loss_a_sum, self.G_ad_loss_b_sum, self.l1_loss_a_sum, self.l1_loss_b_sum,
self.enc_loss_a_sum, self.enc_loss_b_sum])
self.D_loss = tf.summary.merge([self.D_A_loss, self.D_B_loss, self.all_D_loss])
self.init_op = tf.global_variables_initializer()
def train(self, args):
"""Train SG-GAN"""
self.sess.run(self.init_op)
self.writer = tf.summary.FileWriter(args.model_dir, self.sess.graph)
if args.pretrained_path is not None:
self.saverPart.restore(self.sess, args.pretrained_path)
print('restored from pretrained_path:', args.pretrained_path)
if args.pretrained_vgg_path is not None:
self.saverVggPart.restore(self.sess, args.pretrained_vgg_path)
print('restored from pretrained_vgg_path:', args.pretrained_vgg_path)
counter = args.global_step
start_time = time.time()
if 0==args.global_step:
if args.continue_train and self.load_last_ckpt(args.model_dir):
print(" [*] load_last_ckpt SUCCESS")
else:
print(" [!] load_last_ckpt Failed...")
else:
## global_step is set manually
if args.continue_train and self.load_ckpt(args.model_dir, args.global_step):
print(" [*] load_ckpt SUCCESS")
else:
print(" [!] load_ckpt Failed...")
for epoch in range(args.epoch):
# batch_idxs = self.sample_num // self.batch_size
batch_idxs = self.sample_num
# lr = args.lr if epoch < args.epoch_step else args.lr*(args.epoch-epoch)/(args.epoch-args.epoch_step)
# idx = int(epoch/len(self.weights_schedule))
# loss_weights = self.weights_schedule[idx]
for idx in range(0, batch_idxs):
step_ph = epoch*batch_idxs + idx
num_steps = args.epoch*batch_idxs
lr = args.lr*((1 - counter / num_steps)**0.9)
# Update D
_, d_loss, summary_str = self.sess.run([self.D_optim, self.Discriminator_loss, self.D_loss], \
feed_dict={self.is_training : True, self.lr : lr})
self.writer.add_summary(summary_str, counter)
# Update G
for g_iter in range(args.G_update):
_, g_loss, summary_str = self.sess.run([self.G_optim, self.Generator_loss, self.G_loss], \
feed_dict={self.is_training : True, self.lr : lr})
self.writer.add_summary(summary_str, counter)
counter += 1
if np.mod(counter, args.print_freq) == 1:
# display training status
print("Epoch: [%2d] [%4d/%4d] time: %4.4f d_loss: %.8f, g_loss: %.8f" \
% (epoch, idx, batch_idxs, time.time() - start_time, d_loss, g_loss))
self.sample_model(args.sample_dir, epoch, counter)
if (counter>3000 and np.mod(counter, args.save_freq) == 2) or (idx==batch_idxs-1):
self.save(args.model_dir, counter)
def sample_model(self, sample_dir, epoch, idx):
img_name_A, img_name_B, real_A, real_B, x_aa, x_bb = self.sess.run(
[self.img_name_A, self.img_name_B, self.real_A, self.real_B, self.x_aa, self.x_bb], \
feed_dict={self.is_training : False}
)
real_A = unprocess_image(real_A, 127.5, 127.5)
real_B = unprocess_image(real_B, 127.5, 127.5)
x_aa = unprocess_image(x_aa, 127.5, 127.5)
x_bb = unprocess_image(x_bb, 127.5, 127.5)
img_name_A = img_name_A[0]
img_name_B = img_name_B[0]
save_images(real_A, [real_A.shape[0], 1],
'{}/real_A_{:02d}_{:04d}_{}.png'.format(sample_dir, epoch, idx, img_name_B.decode("utf-8").split(".")[0]))
save_images(real_B, [real_B.shape[0], 1],
'{}/real_B_{:02d}_{:04d}_{}.png'.format(sample_dir, epoch, idx, img_name_A.decode("utf-8").split(".")[0]))
save_images(x_aa, [x_aa.shape[0], 1],
'{}/x_aa_{:02d}_{:04d}_{}.png'.format(sample_dir, epoch, idx, img_name_B.decode("utf-8").split(".")[0]))
save_images(x_bb, [x_bb.shape[0], 1],
'{}/x_bb_{:02d}_{:04d}_{}.png'.format(sample_dir, epoch, idx, img_name_A.decode("utf-8").split(".")[0]))
class UNIT_MultiEncSpecificBranchFromImg_Cycle_ChangeRes_FeaMask_VggStyleContentLoss(UNIT):
def init_net(self, args):
assert args.pretrained_path is not None
if args.pretrained_path is not None:
var = tf.get_collection(tf.GraphKeys.VARIABLES, scope='VAEGAN_recon_A') \
+ tf.get_collection(tf.GraphKeys.VARIABLES, scope='VAEGAN_recon_B')
var = var_filter_by_exclude(var, exclude_scopes=['Adam'])
self.saverPart = tf.train.Saver(var, max_to_keep=5)
if args.test_model_path is not None:
var = tf.get_collection(tf.GraphKeys.VARIABLES, scope='UNIT')
var = var_filter_by_exclude(var, exclude_scopes=['Adam'])
self.saverTest = tf.train.Saver(var, max_to_keep=5)
self.summary_writer = tf.summary.FileWriter(args.model_dir)
sv = tf.train.Supervisor(logdir=args.model_dir, is_chief=True, saver=None, summary_op=None,
summary_writer=self.summary_writer, save_model_secs=0, ready_for_local_init_op=None)
if args.phase == 'train':
gpu_options = tf.GPUOptions(allow_growth=False)
else:
gpu_options = tf.GPUOptions(allow_growth=False)
sess_config = tf.ConfigProto(allow_soft_placement=True, gpu_options=gpu_options)
self.sess = sv.prepare_or_wait_for_session(config=sess_config)
def encoder_spec(self, x, is_training=True, reuse=False, scope="part1"):
channel = self.ngf
feaMap_list, gamma_list, beta_list = [], [], []
with tf.variable_scope(scope, reuse=reuse) :
x = ops_EGSCIT.conv(x, channel, kernel=7, stride=1, pad=3, normal_weight_init=self.normal_weight_init, activation_fn='leaky', scope='conv_0')
for i in range(1, self.n_encoder) :
x = ops_EGSCIT.conv(x, channel*2, kernel=3, stride=2, pad=1, normal_weight_init=self.normal_weight_init, activation_fn='leaky', scope='conv_'+str(i))
channel *= 2
# channel = 256
for i in range(0, self.n_enc_resblock) :
x = ops_EGSCIT.resblock(x, channel, kernel=3, stride=1, pad=1, dropout_ratio=self.res_dropout,
normal_weight_init=self.normal_weight_init,
is_training=is_training, norm_fn=self.norm, scope='resblock_'+str(i))
feaMap_list.append(x)
mean, var = tf.nn.moments(x, [1,2])
gamma = mean - 1.
beta = var
gamma_list.append(gamma)
beta_list.append(beta)
return feaMap_list, gamma_list, beta_list
def ins_specific_branch(self, x, is_training=False, scope='VAEGAN_recon', reuse=False):
with tf.variable_scope(scope, reuse=reuse):
feaMap_list, gamma_list, beta_list = self.encoder_spec(x, is_training, scope="part1")
return feaMap_list, gamma_list, beta_list
def apply_feaMap_mask(self, x, feaMap):
# mask = (tf.sign(feaMap)+1.0)/2.0 ## select the >0 elements as mask
mask = tf.sigmoid(feaMap) ## select the >0 elements as mask
# mask = mask/2. - 1 ## norm [0.5, 1] to [0, 1]
mask = mask/2. + 1./2. ## norm [0.5, 1] to [0.75, 1]
# mask = mask/5.*2. + 0.6 ## norm [0.5, 1] to [0.8, 1]