def main():
size = FLAGS.img_size
# debug
if len(FLAGS.train_dir) < 1:
bn_name = ["nobn", "caffebn", "simplebn", "defaultbn", "cbn"]
FLAGS.train_dir = os.path.join(
"logs", FLAGS.model_name + "_" + bn_name[FLAGS.bn] + "_" +
str(FLAGS.phases))
if FLAGS.cgan:
# the label file is npy format
npy_dir = FLAGS.data_dir.replace(".zip", "") + '.npy'
else:
npy_dir = None
if "celeb" in FLAGS.data_dir:
dataset = dataloader.CelebADataset(FLAGS.data_dir,
img_size=(size, size),
npy_dir=npy_dir)
elif "cityscapes" in FLAGS.data_dir:
augmentations = Compose([
RandomCrop(size * 4),
Scale(size * 2),
RandomRotate(10),
RandomHorizontallyFlip(),
RandomSizedCrop(size)
])
dataset = dataloader.cityscapesLoader(FLAGS.data_dir,
is_transform=True,
augmentations=augmentations,
img_size=(size, size))
FLAGS.batch_size /= 64
else:
dataset = dataloader.FileDataset(FLAGS.data_dir,
npy_dir=npy_dir,
img_size=(size, size))
dl = dataloader.TFDataloader(dataset, FLAGS.batch_size,
dataset.file_num // FLAGS.batch_size)
# TF Input
x_fake_sample = tf.placeholder(tf.float32, [None, size, size, 3],
name="x_fake_sample")
x_real = tf.placeholder(tf.float32, [None, size, size, 3], name="x_real")
s_real = tf.placeholder(tf.float32, [None, size, size, 3], name='s_real')
z_noise = tf.placeholder(tf.float32, [None, 128], name="z_noise")
if FLAGS.cgan:
c_noise = tf.placeholder(tf.float32, [None, dataset.class_num],
name="c_noise")
c_label = tf.placeholder(tf.float32, [None, dataset.class_num],
name="c_label")
gen_input = [z_noise, c_noise]
else:
gen_input = z_noise
# look up the config function from lib.config module
gen_model, disc_model = getattr(config,
FLAGS.model_name)(FLAGS.img_size,
dataset.class_num)
disc_model.norm_mtd = FLAGS.bn
x_fake = gen_model(gen_input, update_collection=None)
gen_model.set_reuse()
gen_model.x_fake = x_fake
disc_model.set_label(c_noise)
if FLAGS.phases > 1:
disc_model.set_phase("fake")
else:
disc_model.set_phase("default")
disc_fake, fake_cls_logits = disc_model(x_fake, update_collection=None)
disc_model.set_reuse()
disc_model.set_label(c_label)
if FLAGS.phases > 1:
disc_model.set_phase("real")
else:
disc_model.set_phase("default")
disc_real, real_cls_logits = disc_model(x_real, update_collection=None)
disc_model.disc_real = disc_real
disc_model.disc_fake = disc_fake
disc_model.real_cls_logits = real_cls_logits
disc_model.fake_cls_logits = fake_cls_logits
int_sum_op = []
if FLAGS.use_cache:
disc_fake_sample = disc_model(x_fake_sample)[0]
disc_cost_sample = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
logits=disc_fake_sample,
labels=tf.zeros_like(disc_fake_sample)),
name="cost_disc_fake_sample")
disc_cost_sample_sum = tf.summary.scalar("disc_sample",
disc_cost_sample)
fake_sample_grid = ops.get_grid_image_summary(x_fake_sample, 4)
int_sum_op.append(tf.summary.image("fake sample", fake_sample_grid))
sample_method = [disc_cost_sample, disc_cost_sample_sum, x_fake_sample]
else:
sample_method = None
grid_x_fake = ops.get_grid_image_summary(gen_model.x_fake, 4)
int_sum_op.append(tf.summary.image("generated image", grid_x_fake))
grid_x_real = ops.get_grid_image_summary(x_real, 4)
int_sum_op.append(tf.summary.image("real image", grid_x_real))
int_sum_op = tf.summary.merge(int_sum_op)
raw_gen_cost, raw_disc_real, raw_disc_fake = loss.hinge_loss(
gen_model, disc_model, adv_weight=1.0, summary=False)
disc_model.disc_real_loss = raw_disc_real
disc_model.disc_fake_loss = raw_disc_fake
if FLAGS.cgan:
real_cls_cost, fake_cls_cost = loss.classifier_loss(gen_model,
disc_model,
x_real,
c_label,
c_noise,
weight=1.0 /
dataset.class_num,
summary=False)
step_sum_op = []
subloss_names = ["fake_cls", "real_cls", "gen", "disc_real", "disc_fake"]
sublosses = [
fake_cls_cost, real_cls_cost, raw_gen_cost, raw_disc_real,
raw_disc_fake
]
for n, l in zip(subloss_names, sublosses):
step_sum_op.append(tf.summary.scalar(n, l))
step_sum_op = tf.summary.merge(step_sum_op)
ModelTrainer = trainer.base_gantrainer.BaseGANTrainer( #trainer.separated_gantrainer.SeparatedGANTrainer(#
int_sum_op=int_sum_op,
step_sum_op=step_sum_op,
dataloader=dl,
FLAGS=FLAGS,
gen_model=gen_model,
disc_model=disc_model,
gen_input=gen_input,
x_real=x_real,
label=c_label,
sample_method=sample_method)
#command_controller = trainer.cmd_ctrl.CMDControl(ModelTrainer)
#command_controller.start_thread()
print("=> Build train op")
ModelTrainer.build_train_op()
print("=> ##### Generator Variable #####")
gen_model.print_trainble_vairables()
print("=> ##### Discriminator Variable #####")
disc_model.print_trainble_vairables()
print("=> ##### All Variable #####")
for v in tf.trainable_variables():
print("%s\t\t\t\t%s" % (v.name, str(v.get_shape().as_list())))
print("=> #### Moving Variable ####")
for v in tf.global_variables():
if "moving" in v.name:
print("%s\t\t\t\t%s" % (v.name, str(v.get_shape().as_list())))
ModelTrainer.init_training()
ModelTrainer.train()
sys.path.insert(0, ".")
import torch
from torch.utils.data import DataLoader
import matplotlib.pyplot as plt
from lib import dataloader
from lib.ptseg import *
print(Compose)
augmentations = Compose(
[Scale(512),
RandomRotate(10),
RandomHorizontallyFlip(),
RandomCrop(128)])
local_path = "/home/atlantix/data/cityscapes/"
dst = dataloader.cityscapesLoader(local_path,
is_transform=True,
augmentations=augmentations)
bs = 4
trainloader = DataLoader(dst, batch_size=bs, num_workers=0)
for i, data in enumerate(trainloader):
imgs, labels = data
import pdb
pdb.set_trace()
imgs = imgs.numpy()[:, ::-1, :, :]
imgs = np.transpose(imgs, [0, 2, 3, 1])
f, axarr = plt.subplots(bs, 2)
for j in range(bs):
axarr[j][0].imshow(imgs[j])
axarr[j][1].imshow(dst.decode_segmap(labels.numpy()[j]))
plt.show()
a = raw_input()
def main():
size = FLAGS.img_size
if FLAGS.cgan:
npy_dir = FLAGS.data_dir.replace(".zip", "") + '.npy'
else:
npy_dir = None
if "celeb" in FLAGS.data_dir:
dataset = dataloader.CelebADataset(FLAGS.data_dir,
img_size=(size, size),
npy_dir=npy_dir)
elif "cityscapes" in FLAGS.data_dir:
augmentations = Compose([
RandomCrop(size * 4),
Scale(size * 2),
RandomRotate(10),
RandomHorizontallyFlip(),
RandomSizedCrop(size)
])
dataset = dataloader.cityscapesLoader(FLAGS.data_dir,
is_transform=True,
augmentations=augmentations,
img_size=(size, size))
else:
dataset = dataloader.FileDataset(FLAGS.data_dir,
npy_dir=npy_dir,
img_size=(size, size),
shuffle=True)
dl = DataLoader(dataset,
batch_size=FLAGS.batch_size // 64,
shuffle=True,
num_workers=NUM_WORKER,
collate_fn=dataloader.default_collate)
# TF Input
x_fake_sample = tf.placeholder(tf.float32, [None, size, size, 3],
name="x_fake_sample")
x_real = tf.placeholder(tf.float32, [None, size, size, 3], name="x_real")
s_real = tf.placeholder(tf.float32, [None, size, size, 3], name='s_real')
z_noise = tf.placeholder(tf.float32, [None, 128], name="z_noise")
if FLAGS.cgan:
c_noise = tf.placeholder(tf.float32, [None, dataset.class_num],
name="c_noise")
c_label = tf.placeholder(tf.float32, [None, dataset.class_num],
name="c_label")
gen_input = [z_noise, c_noise]
else:
gen_input = z_noise
gen_model, disc_model = getattr(config,
FLAGS.model_name)(FLAGS.img_size,
dataset.class_num)
gen_model.cbn_project = FLAGS.cbn_project
x_fake = gen_model(gen_input, update_collection=None)
gen_model.set_reuse()
gen_model.x_fake = x_fake
disc_real, real_cls_logits = disc_model(x_real, update_collection=None)
disc_model.set_reuse()
disc_fake, fake_cls_logits = disc_model(x_fake, update_collection="no_ops")
disc_model.disc_real = disc_real
disc_model.disc_fake = disc_fake
disc_model.real_cls_logits = real_cls_logits
disc_model.fake_cls_logits = fake_cls_logits
int_sum_op = []
if FLAGS.use_cache:
disc_fake_sample = disc_model(x_fake_sample)[0]
disc_cost_sample = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
logits=disc_fake_sample,
labels=tf.zeros_like(disc_fake_sample)),
name="cost_disc_fake_sample")
disc_cost_sample_sum = tf.summary.scalar("disc_sample",
disc_cost_sample)
fake_sample_grid = ops.get_grid_image_summary(x_fake_sample, 4)
int_sum_op.append(tf.summary.image("fake sample", fake_sample_grid))
sample_method = [disc_cost_sample, disc_cost_sample_sum, x_fake_sample]
else:
sample_method = None
grid_x_fake = ops.get_grid_image_summary(gen_model.x_fake, 4)
int_sum_op.append(tf.summary.image("generated image", grid_x_fake))
grid_x_real = ops.get_grid_image_summary(x_real, 4)
int_sum_op.append(tf.summary.image("real image", grid_x_real))
if FLAGS.cgan:
loss.classifier_loss(gen_model,
disc_model,
x_real,
c_label,
c_noise,
weight=1.0)
loss.hinge_loss(gen_model, disc_model, adv_weight=1.0)
int_sum_op = tf.summary.merge(int_sum_op)
ModelTrainer = trainer.base_gantrainer.BaseGANTrainer(
int_sum_op=int_sum_op,
dataloader=dl,
FLAGS=FLAGS,
gen_model=gen_model,
disc_model=disc_model,
gen_input=gen_input,
x_real=x_real,
label=c_label,
sample_method=sample_method)
command_controller = trainer.cmd_ctrl.CMDControl(ModelTrainer)
command_controller.start_thread()
print("=> Build train op")
ModelTrainer.build_train_op()
print("=> ##### Generator Variable #####")
gen_model.print_trainble_vairables()
print("=> ##### Discriminator Variable #####")
disc_model.print_trainble_vairables()
print("=> ##### All Variable #####")
for v in tf.trainable_variables():
print("%s\t\t\t\t%s" % (v.name, str(v.get_shape().as_list())))
print("=> #### Moving Variable ####")
for v in tf.global_variables():
if "moving" in v.name:
print("%s\t\t\t\t%s" % (v.name, str(v.get_shape().as_list())))
ModelTrainer.init_training()
ModelTrainer.train()
def main():
size = FLAGS.img_size
if len(FLAGS.train_dir) < 1:
# if the train dir is not set, then automatically decide one
FLAGS.train_dir = os.path.join("logs",
FLAGS.model_name + str(FLAGS.img_size))
if FLAGS.cgan:
FLAGS.train_dir += "_cgan"
if FLAGS.cgan:
# the label file should be npy format
npy_dir = FLAGS.data_dir.replace(".zip", "") + '.npy'
else:
npy_dir = None
if "celeb" in FLAGS.data_dir:
dataset = dataloader.CelebADataset(FLAGS.data_dir,
img_size=(size, size),
npy_dir=npy_dir)
elif "cityscapes" in FLAGS.data_dir:
# outdated
augmentations = Compose([
RandomCrop(size * 4),
Scale(size * 2),
RandomRotate(10),
RandomHorizontallyFlip(),
RandomSizedCrop(size)
])
dataset = dataloader.cityscapesLoader(FLAGS.data_dir,
is_transform=True,
augmentations=augmentations,
img_size=(size, size))
FLAGS.batch_size /= 64
else:
dataset = dataloader.FileDataset(FLAGS.data_dir,
npy_dir=npy_dir,
img_size=(size, size))
dl = dataloader.TFDataloader(dataset, FLAGS.batch_size,
dataset.file_num // FLAGS.batch_size)
# TF Input
x_fake_sample = tf.placeholder(tf.float32, [None, size, size, 3],
name="x_fake_sample")
x_real = tf.placeholder(tf.float32, [None, size, size, 3], name="x_real")
s_real = tf.placeholder(tf.float32, [None, size, size, 3], name='s_real')
z_noise = tf.placeholder(tf.float32, [None, 128], name="z_noise")
if FLAGS.cgan:
c_noise = tf.placeholder(tf.float32, [None, dataset.class_num],
name="c_noise")
c_label = tf.placeholder(tf.float32, [None, dataset.class_num],
name="c_label")
gen_input = [z_noise, c_noise]
else:
gen_input = z_noise
c_label = c_noise = None
# look up the config function from lib.config module
gen_model, disc_model = getattr(config,
FLAGS.model_name)(FLAGS.img_size,
dataset.class_num)
gen_model.label = c_noise
x_fake = gen_model(gen_input)
gen_model.set_reuse()
gen_model.x_fake = x_fake
disc_model.label = c_noise
disc_fake, fake_cls_logits = disc_model(x_fake)
disc_model.set_reuse()
disc_model.label = c_label
disc_real, real_cls_logits = disc_model(x_real)
disc_model.disc_real = disc_real
disc_model.disc_fake = disc_fake
disc_model.real_cls_logits = real_cls_logits
disc_model.fake_cls_logits = fake_cls_logits
raw_gen_cost, raw_disc_real, raw_disc_fake = loss.hinge_loss(
gen_model, disc_model, adv_weight=1.0, summary=False)
disc_model.disc_real_loss = raw_disc_real
disc_model.disc_fake_loss = raw_disc_fake
if FLAGS.cgan:
real_cls_cost, fake_cls_cost = loss.classifier_loss(gen_model,
disc_model,
x_real,
c_label,
c_noise,
weight=1.0 /
dataset.class_num,
summary=False)
subloss_names = [
"fake_cls", "real_cls", "gen", "disc_real", "disc_fake"
]
sublosses = [
fake_cls_cost, real_cls_cost, raw_gen_cost, raw_disc_real,
raw_disc_fake
]
else:
subloss_names = ["gen", "disc_real", "disc_fake"]
sublosses = [raw_gen_cost, raw_disc_real, raw_disc_fake]
step_sum_op = [] # summary at every step
for n, l in zip(subloss_names, sublosses):
step_sum_op.append(tf.summary.scalar(n, l))
if gen_model.debug or disc_model.debug:
for model_var in tf.global_variables():
if gen_model.name in model_var.op.name or disc_model.name in model_var.op.name:
step_sum_op.append(
tf.summary.histogram(model_var.op.name, model_var))
step_sum_op = tf.summary.merge(step_sum_op)
int_sum_op = [] # summary at some interval
grid_x_fake = ops.get_grid_image_summary(gen_model.x_fake, 4)
int_sum_op.append(tf.summary.image("generated image", grid_x_fake))
grid_x_real = ops.get_grid_image_summary(x_real, 4)
int_sum_op.append(tf.summary.image("real image", grid_x_real))
int_sum_op = tf.summary.merge(int_sum_op)
ModelTrainer = trainer.base_gantrainer.BaseGANTrainer(
int_sum_op=int_sum_op,
step_sum_op=step_sum_op,
dataloader=dl,
FLAGS=FLAGS,
gen_model=gen_model,
disc_model=disc_model,
gen_input=gen_input,
x_real=x_real,
label=c_label)
print("=> Build train op")
ModelTrainer.build_train_op()
print("=> ##### Generator Variable #####")
gen_model.print_trainble_vairables()
print("=> ##### Discriminator Variable #####")
disc_model.print_trainble_vairables()
print("=> #### Moving Variable ####")
for v in tf.global_variables():
if "moving" in v.name:
print("%s\t\t\t\t%s" % (v.name, str(v.get_shape().as_list())))
print("=> #### Generator update dependency ####")
for v in gen_model.update_ops:
print("%s" % (v.name))
print("=> #### Discriminator update dependency ####")
for v in disc_model.update_ops:
print("%s" % (v.name))
ModelTrainer.init_training()
ModelTrainer.train()
def main():
size = FLAGS.img_size
if FLAGS.cgan:
# the label file is npy format
npy_dir = FLAGS.data_dir.replace(".zip", "") + '.npy'
else:
npy_dir = None
if "celeb" in FLAGS.data_dir:
dataset = dataloader.CelebADataset(FLAGS.data_dir,
img_size=(size, size),
npy_dir=npy_dir)
elif "cityscapes" in FLAGS.data_dir:
augmentations = Compose([
RandomCrop(size * 4),
Scale(size * 2),
RandomRotate(10),
RandomHorizontallyFlip(),
RandomSizedCrop(size)
])
dataset = dataloader.cityscapesLoader(FLAGS.data_dir,
is_transform=True,
augmentations=augmentations,
img_size=(size, size))
FLAGS.batch_size /= 64
else:
dataset = dataloader.FileDataset(FLAGS.data_dir,
npy_dir=npy_dir,
img_size=(size, size))
dl = dataloader.TFDataloader(dataset, FLAGS.batch_size,
dataset.file_num // FLAGS.batch_size)
# TF Input
x_fake_sample = tf.placeholder(tf.float32, [None, size, size, 3],
name="x_fake_sample")
x_real = tf.placeholder(tf.float32, [None, size, size, 3], name="x_real")
s_real = tf.placeholder(tf.float32, [None, size, size, 3], name='s_real')
z_noise = tf.placeholder(tf.float32, [None, 128], name="z_noise")
if FLAGS.cgan:
c_noise = tf.placeholder(tf.float32, [None, dataset.class_num],
name="c_noise")
c_label = tf.placeholder(tf.float32, [None, dataset.class_num],
name="c_label")
gen_input = [z_noise, c_noise]
else:
gen_input = z_noise
# look up the config function from lib.config module
gen_model, disc_model = getattr(config,
FLAGS.model_name)(FLAGS.img_size,
dataset.class_num)
gen_model.cbn_project = FLAGS.cbn_project
gen_model.spectral_norm = FLAGS.sn
disc_model.cbn_project = FLAGS.cbn_project
disc_model.spectral_norm = FLAGS.sn
ModelTrainer = trainer.base_gantrainer.BaseGANTrainer(
step_sum_op=None,
int_sum_op=None,
dataloader=dl,
FLAGS=FLAGS,
gen_model=gen_model,
disc_model=disc_model,
gen_input=gen_input,
x_real=x_real,
label=c_label)
g_tower_grads = []
d_tower_grads = []
g_optim = tf.train.AdamOptimizer(learning_rate=ModelTrainer.g_lr,
beta1=0.,
beta2=0.9)
d_optim = tf.train.AdamOptimizer(learning_rate=ModelTrainer.d_lr,
beta1=0.,
beta2=0.9)
grad_x = []
grad_x_name = []
xs = []
x_name = []
def tower(gpu_id,
gen_input,
x_real,
c_label=None,
c_noise=None,
update_collection=None,
loss_collection=[]):
"""
The loss function builder of gen and disc
"""
gen_model.cost = disc_model.cost = 0
gen_model.set_phase("gpu%d" % gpu_id)
x_fake = gen_model(gen_input, update_collection=update_collection)
gen_model.set_reuse()
gen_model.x_fake = x_fake
disc_model.set_phase("gpu%d" % gpu_id)
disc_real, real_cls_logits = disc_model(
x_real, update_collection=update_collection)
disc_model.set_reuse()
disc_model.recorded_tensors = []
disc_model.recorded_names = []
disc_fake, fake_cls_logits = disc_model(
x_fake, update_collection=update_collection)
disc_model.disc_real = disc_real
disc_model.disc_fake = disc_fake
disc_model.real_cls_logits = real_cls_logits
disc_model.fake_cls_logits = fake_cls_logits
if FLAGS.cgan:
fake_cls_cost, real_cls_cost = loss.classifier_loss(
gen_model,
disc_model,
x_real,
c_label,
c_noise,
weight=1.0 / dataset.class_num,
summary=False)
raw_gen_cost, raw_disc_real, raw_disc_fake = loss.hinge_loss(
gen_model, disc_model, adv_weight=1.0, summary=False)
gen_model.vars = [
v for v in tf.trainable_variables() if gen_model.name in v.name
]
disc_model.vars = [
v for v in tf.trainable_variables() if disc_model.name in v.name
]
g_grads = tf.gradients(gen_model.cost,
gen_model.vars,
colocate_gradients_with_ops=True)
d_grads = tf.gradients(disc_model.cost,
disc_model.vars,
colocate_gradients_with_ops=True)
g_grads = [
tf.check_numerics(g, "G grad nan: " + str(g)) for g in g_grads
]
d_grads = [
tf.check_numerics(g, "D grad nan: " + str(g)) for g in d_grads
]
g_tower_grads.append(g_grads)
d_tower_grads.append(d_grads)
tensors = gen_model.recorded_tensors + disc_model.recorded_tensors
names = gen_model.recorded_names + disc_model.recorded_names
if gpu_id == 0: x_name.extend(names)
xs.append(tensors)
names = names[::-1]
tensors = tensors[::-1]
grads = tf.gradients(disc_fake,
tensors,
colocate_gradients_with_ops=True)
for n, g in zip(names, grads):
print(n, g)
grad_x.append(
[tf.check_numerics(g, "BP nan: " + str(g)) for g in grads])
if gpu_id == 0: grad_x_name.extend(names)
disc_model.recorded_tensors = []
disc_model.recorded_names = []
gen_model.recorded_tensors = []
gen_model.recorded_names = []
return gen_model.cost, disc_model.cost, [
fake_cls_cost, real_cls_cost, raw_gen_cost, raw_disc_real,
raw_disc_fake
]
def average_gradients(tower_grads):
average_grads = []
num_gpus = len(tower_grads)
num_items = len(tower_grads[0])
for i in range(num_items):
average_grads.append(0.0)
for j in range(num_gpus):
average_grads[i] += tower_grads[j][i]
average_grads[i] /= num_gpus
return average_grads
sbs = FLAGS.batch_size // NUM_GPU
for i in range(NUM_GPU):
if i == 0:
update_collection = None
else:
update_collection = "no_ops"
with tf.device(tf.DeviceSpec(device_type="GPU", device_index=i)):
if FLAGS.cgan:
l1, l2, ot1 = tower(i, [
z_noise[sbs * i:sbs * (i + 1)], c_noise[sbs * i:sbs *
(i + 1)]
],
x_real[sbs * i:sbs * (i + 1)],
c_label[sbs * i:sbs * (i + 1)],
c_noise[sbs * i:sbs * (i + 1)],
update_collection=update_collection)
else:
l1, l2, ot1 = tower(i,
z_noise[sbs * i:sbs * (i + 1)],
x_real[sbs * i:sbs * (i + 1)],
update_collection=update_collection)
if i == 0:
int_sum_op = []
grid_x_fake = ops.get_grid_image_summary(gen_model.x_fake, 4)
int_sum_op.append(tf.summary.image("generated image", grid_x_fake))
grid_x_real = ops.get_grid_image_summary(x_real, 4)
int_sum_op.append(tf.summary.image("real image", grid_x_real))
step_sum_op = []
sub_loss_names = [
"fake_cls", "real_cls", "gen", "disc_real", "disc_fake"
]
for n, l in zip(sub_loss_names, ot1):
step_sum_op.append(tf.summary.scalar(n, l))
step_sum_op.append(tf.summary.scalar("gen", gen_model.cost))
step_sum_op.append(tf.summary.scalar("disc", disc_model.cost))
with tf.device(tf.DeviceSpec(device_type="GPU", device_index=0)):
g_grads = average_gradients(g_tower_grads)
d_grads = average_gradients(d_tower_grads)
gen_model.update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS,
scope=gen_model.name + "/")
disc_model.update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS,
scope=disc_model.name + "/")
print(gen_model.update_ops)
print(disc_model.update_ops)
def merge_list(l1, l2):
return [[l1[i], l2[i]] for i in range(len(l1))]
with tf.control_dependencies(gen_model.update_ops):
gen_model.train_op = g_optim.apply_gradients(
merge_list(g_grads, gen_model.vars))
with tf.control_dependencies(disc_model.update_ops):
disc_model.train_op = d_optim.apply_gradients(
merge_list(d_grads, disc_model.vars))
if FLAGS.use_cache:
disc_fake_sample = disc_model(x_fake_sample)[0]
disc_cost_sample = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
logits=disc_fake_sample,
labels=tf.zeros_like(disc_fake_sample)),
name="cost_disc_fake_sample")
disc_cost_sample_sum = tf.summary.scalar("disc_sample",
disc_cost_sample)
fake_sample_grid = ops.get_grid_image_summary(x_fake_sample, 4)
int_sum_op.append(tf.summary.image("fake sample", fake_sample_grid))
sample_method = [disc_cost_sample, disc_cost_sample_sum, x_fake_sample]
else:
sample_method = None
ModelTrainer.int_sum_op = tf.summary.merge(int_sum_op)
ModelTrainer.step_sum_op = tf.summary.merge(step_sum_op)
ModelTrainer.grad_x = grad_x
ModelTrainer.grad_x_name = grad_x_name
ModelTrainer.xs = xs
ModelTrainer.x_name = x_name
#command_controller = trainer.cmd_ctrl.CMDControl(ModelTrainer)
#command_controller.start_thread()
print("=> Build train op")
# ModelTrainer.build_train_op()
print("=> ##### Generator Variable #####")
gen_model.print_variables()
print("=> ##### Discriminator Variable #####")
disc_model.print_variables()
print("=> ##### All Variable #####")
for v in tf.trainable_variables():
print("%s\t\t\t\t%s" % (v.name, str(v.get_shape().as_list())))
print("=> #### Moving Variable ####")
for v in tf.global_variables():
if "moving" in v.name:
print("%s\t\t\t\t%s" % (v.name, str(v.get_shape().as_list())))
ModelTrainer.init_training()
ModelTrainer.train()