def __init__(self, image_real, label_org, label_trg, cfg, step_per_epoch):
self.program = fluid.default_main_program().clone()
with fluid.program_guard(self.program):
model = StarGAN_model()
self.fake_img = model.network_G(image_real,
label_trg,
cfg,
name="g_main")
self.rec_img = model.network_G(self.fake_img,
label_org,
cfg,
name="g_main")
self.infer_program = self.program.clone(for_test=False)
self.g_loss_rec = fluid.layers.reduce_mean(
fluid.layers.abs(
fluid.layers.elementwise_sub(x=image_real,
y=self.rec_img)))
self.pred_fake, self.cls_fake = model.network_D(self.fake_img,
cfg,
name="d_main")
if cfg.gan_mode != 'wgan':
raise NotImplementedError(
"gan_mode {} is not support! only support wgan".format(
cfg.gan_mode))
#wgan
self.g_loss_fake = -1 * fluid.layers.mean(self.pred_fake)
cls_shape = self.cls_fake.shape
self.cls_fake = fluid.layers.reshape(
self.cls_fake,
[-1, cls_shape[1] * cls_shape[2] * cls_shape[3]])
self.g_loss_cls = fluid.layers.reduce_sum(
fluid.layers.sigmoid_cross_entropy_with_logits(
self.cls_fake, label_trg)) / cfg.batch_size
self.g_loss = self.g_loss_fake + cfg.lambda_rec * self.g_loss_rec + self.g_loss_cls
lr = cfg.g_lr
vars = []
for var in self.program.list_vars():
if fluid.io.is_parameter(var) and var.name.startswith("g_"):
vars.append(var.name)
self.param = vars
total_iters = step_per_epoch * cfg.epoch
boundaries = [cfg.num_iters - cfg.num_iters_decay]
values = [lr]
for x in range(cfg.num_iters - cfg.num_iters_decay + 1,
total_iters):
if x % cfg.lr_update_step == 0:
boundaries.append(x)
lr -= (lr / float(cfg.num_iters_decay))
values.append(lr)
lr = values[-1]
lr -= (lr / float(cfg.num_iters_decay))
values.append(lr)
optimizer = fluid.optimizer.Adam(
learning_rate=fluid.layers.piecewise_decay(
boundaries=boundaries, values=values),
beta1=0.5,
beta2=0.999,
name="net_G")
optimizer.minimize(self.g_loss, parameter_list=vars)
def __init__(self, image_real, label_org, label_trg, cfg, step_per_epoch):
self.program = fluid.default_main_program().clone()
with fluid.program_guard(self.program):
model = StarGAN_model()
clone_image_real = []
for b in self.program.blocks:
if b.has_var('image_real'):
clone_image_real = b.var('image_real')
break
self.fake_img = model.network_G(
image_real, label_trg, cfg, name="g_main")
self.pred_real, self.cls_real = model.network_D(
image_real, cfg, name="d_main")
self.pred_fake, _ = model.network_D(
self.fake_img, cfg, name="d_main")
cls_shape = self.cls_real.shape
self.cls_real = fluid.layers.reshape(
self.cls_real,
[-1, cls_shape[1] * cls_shape[2] * cls_shape[3]])
self.d_loss_cls = fluid.layers.reduce_sum(
fluid.layers.sigmoid_cross_entropy_with_logits(
self.cls_real, label_org)) / cfg.batch_size
#wgan
if cfg.gan_mode == "wgan":
self.d_loss_fake = fluid.layers.mean(self.pred_fake)
self.d_loss_real = -1 * fluid.layers.mean(self.pred_real)
self.d_loss_gp = self.gradient_penalty(
getattr(model, "network_D"),
clone_image_real,
self.fake_img,
cfg=cfg,
name="d_main")
self.d_loss = self.d_loss_real + self.d_loss_fake + self.d_loss_cls + cfg.lambda_gp * self.d_loss_gp
#lsgan
elif cfg.gan_mode == "lsgan":
ones = fluid.layers.fill_constant_batch_size_like(
input=self.pred_real,
shape=self.pred_real.shape,
value=1,
dtype='float32')
self.d_loss_real = fluid.layers.mean(
fluid.layers.square(
fluid.layers.elementwise_sub(
x=self.pred_real, y=ones)))
self.d_loss_fake = fluid.layers.mean(
fluid.layers.square(x=self.pred_fake))
self.d_loss = self.d_loss_real + self.d_loss_fake + cfg.lambda_cls * self.d_loss_cls
self.d_loss_real.persistable = True
self.d_loss_fake.persistable = True
self.d_loss_gp.persistable = True
self.d_loss_cls.persistable = True
vars = []
for var in self.program.list_vars():
if fluid.io.is_parameter(var) and var.name.startswith("d_"):
vars.append(var.name)
self.param = vars
total_iters = step_per_epoch * cfg.epoch
boundaries = [cfg.num_iters - cfg.num_iters_decay]
values = [cfg.d_lr]
lr = cfg.d_lr
for x in range(cfg.num_iters - cfg.num_iters_decay + 1,
total_iters):
if x % cfg.lr_update_step == 0:
boundaries.append(x)
lr -= (lr / float(cfg.num_iters_decay))
values.append(lr)
lr = values[-1]
lr -= (lr / float(cfg.num_iters_decay))
values.append(lr)
optimizer = fluid.optimizer.Adam(
learning_rate=fluid.layers.piecewise_decay(
boundaries=boundaries, values=values),
beta1=0.5,
beta2=0.999,
name="net_D")
optimizer.minimize(self.d_loss, parameter_list=vars)
def infer(args):
data_shape = [-1, 3, args.image_size, args.image_size]
input = fluid.layers.data(name='input', shape=data_shape, dtype='float32')
label_org_ = fluid.layers.data(name='label_org_',
shape=[args.c_dim],
dtype='float32')
label_trg_ = fluid.layers.data(name='label_trg_',
shape=[args.c_dim],
dtype='float32')
image_name = fluid.layers.data(name='image_name',
shape=[args.n_samples],
dtype='int32')
model_name = 'net_G'
if args.model_net == 'CycleGAN':
py_reader = fluid.io.PyReader(
feed_list=[input, image_name],
capacity=4, ## batch_size * 4
iterable=True,
use_double_buffer=True)
from network.CycleGAN_network import CycleGAN_model
model = CycleGAN_model()
if args.input_style == "A":
fake = model.network_G(input, name="GA", cfg=args)
elif args.input_style == "B":
fake = model.network_G(input, name="GB", cfg=args)
else:
raise "Input with style [%s] is not supported." % args.input_style
elif args.model_net == 'Pix2pix':
py_reader = fluid.io.PyReader(
feed_list=[input, image_name],
capacity=4, ## batch_size * 4
iterable=True,
use_double_buffer=True)
from network.Pix2pix_network import Pix2pix_model
model = Pix2pix_model()
fake = model.network_G(input, "generator", cfg=args)
elif args.model_net == 'StarGAN':
py_reader = fluid.io.PyReader(
feed_list=[input, label_org_, label_trg_, image_name],
capacity=32,
iterable=True,
use_double_buffer=True)
from network.StarGAN_network import StarGAN_model
model = StarGAN_model()
fake = model.network_G(input, label_trg_, name="g_main", cfg=args)
elif args.model_net == 'STGAN':
from network.STGAN_network import STGAN_model
py_reader = fluid.io.PyReader(
feed_list=[input, label_org_, label_trg_, image_name],
capacity=32,
iterable=True,
use_double_buffer=True)
model = STGAN_model()
fake, _ = model.network_G(input,
label_org_,
label_trg_,
cfg=args,
name='generator',
is_test=True)
elif args.model_net == 'AttGAN':
from network.AttGAN_network import AttGAN_model
py_reader = fluid.io.PyReader(
feed_list=[input, label_org_, label_trg_, image_name],
capacity=32,
iterable=True,
use_double_buffer=True)
model = AttGAN_model()
fake, _ = model.network_G(input,
label_org_,
label_trg_,
cfg=args,
name='generator',
is_test=True)
elif args.model_net == 'CGAN':
noise = fluid.layers.data(name='noise',
shape=[args.noise_size],
dtype='float32')
conditions = fluid.layers.data(name='conditions',
shape=[1],
dtype='float32')
from network.CGAN_network import CGAN_model
model = CGAN_model(args.n_samples)
fake = model.network_G(noise, conditions, name="G")
elif args.model_net == 'DCGAN':
noise = fluid.layers.data(name='noise',
shape=[args.noise_size],
dtype='float32')
from network.DCGAN_network import DCGAN_model
model = DCGAN_model(args.n_samples)
fake = model.network_G(noise, name="G")
else:
raise NotImplementedError("model_net {} is not support".format(
args.model_net))
def _compute_start_end(image_name):
image_name_start = np.array(image_name)[0].astype('int32')
image_name_end = image_name_start + args.n_samples - 1
image_name_save = str(np.array(image_name)[0].astype('int32')) + '.jpg'
print("read {}.jpg ~ {}.jpg".format(image_name_start, image_name_end))
return image_name_save
# prepare environment
place = fluid.CPUPlace()
if args.use_gpu:
place = fluid.CUDAPlace(0)
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
for var in fluid.default_main_program().global_block().all_parameters():
print(var.name)
print(args.init_model + '/' + model_name)
fluid.io.load_persistables(exe, os.path.join(args.init_model, model_name))
print('load params done')
if not os.path.exists(args.output):
os.makedirs(args.output)
attr_names = args.selected_attrs.split(',')
if args.model_net == 'AttGAN' or args.model_net == 'STGAN':
test_reader = celeba_reader_creator(image_dir=args.dataset_dir,
list_filename=args.test_list,
args=args,
mode="VAL")
reader_test = test_reader.make_reader(return_name=True)
py_reader.decorate_batch_generator(
reader_test,
places=fluid.cuda_places() if args.use_gpu else fluid.cpu_places())
for data in py_reader():
real_img, label_org, label_trg, image_name = data[0][
'input'], data[0]['label_org_'], data[0]['label_trg_'], data[
0]['image_name']
image_name_save = _compute_start_end(image_name)
real_img_temp = save_batch_image(np.array(real_img))
images = [real_img_temp]
for i in range(args.c_dim):
label_trg_tmp = copy.deepcopy(np.array(label_trg))
for j in range(len(label_trg_tmp)):
label_trg_tmp[j][i] = 1.0 - label_trg_tmp[j][i]
label_trg_tmp = check_attribute_conflict(
label_trg_tmp, attr_names[i], attr_names)
label_org_tmp = list(
map(lambda x: ((x * 2) - 1) * 0.5, np.array(label_org)))
label_trg_tmp = list(
map(lambda x: ((x * 2) - 1) * 0.5, label_trg_tmp))
if args.model_net == 'AttGAN':
for k in range(len(label_trg_tmp)):
label_trg_tmp[k][i] = label_trg_tmp[k][i] * 2.0
tensor_label_org_ = fluid.LoDTensor()
tensor_label_trg_ = fluid.LoDTensor()
tensor_label_org_.set(label_org_tmp, place)
tensor_label_trg_.set(label_trg_tmp, place)
out = exe.run(feed={
"input": real_img,
"label_org_": tensor_label_org_,
"label_trg_": tensor_label_trg_
},
fetch_list=[fake.name])
fake_temp = save_batch_image(out[0])
images.append(fake_temp)
images_concat = np.concatenate(images, 1)
if len(np.array(label_org)) > 1:
images_concat = np.concatenate(images_concat, 1)
imageio.imwrite(
os.path.join(args.output, "fake_img_" + image_name_save),
((images_concat + 1) * 127.5).astype(np.uint8))
elif args.model_net == 'StarGAN':
test_reader = celeba_reader_creator(image_dir=args.dataset_dir,
list_filename=args.test_list,
args=args,
mode="VAL")
reader_test = test_reader.make_reader(return_name=True)
py_reader.decorate_batch_generator(
reader_test,
places=fluid.cuda_places() if args.use_gpu else fluid.cpu_places())
for data in py_reader():
real_img, label_org, label_trg, image_name = data[0][
'input'], data[0]['label_org_'], data[0]['label_trg_'], data[
0]['image_name']
image_name_save = _compute_start_end(image_name)
real_img_temp = save_batch_image(np.array(real_img))
images = [real_img_temp]
for i in range(args.c_dim):
label_trg_tmp = copy.deepcopy(np.array(label_org))
for j in range(len(np.array(label_org))):
label_trg_tmp[j][i] = 1.0 - label_trg_tmp[j][i]
label_trg_tmp = check_attribute_conflict(
label_trg_tmp, attr_names[i], attr_names)
tensor_label_trg_ = fluid.LoDTensor()
tensor_label_trg_.set(label_trg_tmp, place)
out = exe.run(feed={
"input": real_img,
"label_trg_": tensor_label_trg_
},
fetch_list=[fake.name])
fake_temp = save_batch_image(out[0])
images.append(fake_temp)
images_concat = np.concatenate(images, 1)
if len(np.array(label_org)) > 1:
images_concat = np.concatenate(images_concat, 1)
imageio.imwrite(
os.path.join(args.output, "fake_img_" + image_name_save),
((images_concat + 1) * 127.5).astype(np.uint8))
elif args.model_net == 'Pix2pix' or args.model_net == 'CycleGAN':
test_reader = reader_creator(image_dir=args.dataset_dir,
list_filename=args.test_list,
shuffle=False,
batch_size=args.n_samples,
mode="VAL")
reader_test = test_reader.make_reader(args, return_name=True)
py_reader.decorate_batch_generator(
reader_test,
places=fluid.cuda_places() if args.use_gpu else fluid.cpu_places())
id2name = test_reader.id2name
for data in py_reader():
real_img, image_name = data[0]['input'], data[0]['image_name']
image_name = id2name[np.array(image_name).astype('int32')[0]]
print("read: ", image_name)
fake_temp = exe.run(fetch_list=[fake.name],
feed={"input": real_img})
fake_temp = np.squeeze(fake_temp[0]).transpose([1, 2, 0])
input_temp = np.squeeze(np.array(real_img)[0]).transpose([1, 2, 0])
imageio.imwrite(os.path.join(args.output, "fake_" + image_name),
((fake_temp + 1) * 127.5).astype(np.uint8))
elif args.model_net == 'CGAN':
noise_data = np.random.uniform(low=-1.0,
high=1.0,
size=[args.n_samples, args.noise_size
]).astype('float32')
label = np.random.randint(0, 9, size=[args.n_samples,
1]).astype('float32')
noise_tensor = fluid.LoDTensor()
conditions_tensor = fluid.LoDTensor()
noise_tensor.set(noise_data, place)
conditions_tensor.set(label, place)
fake_temp = exe.run(fetch_list=[fake.name],
feed={
"noise": noise_tensor,
"conditions": conditions_tensor
})[0]
fake_image = np.reshape(fake_temp, (args.n_samples, -1))
fig = utility.plot(fake_image)
plt.savefig(os.path.join(args.output, 'fake_cgan.png'),
bbox_inches='tight')
plt.close(fig)
elif args.model_net == 'DCGAN':
noise_data = np.random.uniform(low=-1.0,
high=1.0,
size=[args.n_samples, args.noise_size
]).astype('float32')
noise_tensor = fluid.LoDTensor()
noise_tensor.set(noise_data, place)
fake_temp = exe.run(fetch_list=[fake.name],
feed={"noise": noise_tensor})[0]
fake_image = np.reshape(fake_temp, (args.n_samples, -1))
fig = utility.plot(fake_image)
plt.savefig(os.path.join(args.output, 'fake_dcgan.png'),
bbox_inches='tight')
plt.close(fig)
else:
raise NotImplementedError("model_net {} is not support".format(
args.model_net))
def infer(args):
data_shape = [-1, 3, args.image_size, args.image_size]
input = fluid.layers.data(name='input', shape=data_shape, dtype='float32')
label_org_ = fluid.layers.data(
name='label_org_', shape=[args.c_dim], dtype='float32')
label_trg_ = fluid.layers.data(
name='label_trg_', shape=[args.c_dim], dtype='float32')
model_name = 'net_G'
if args.model_net == 'CycleGAN':
from network.CycleGAN_network import CycleGAN_model
model = CycleGAN_model()
if args.input_style == "A":
fake = model.network_G(input, name="GA", cfg=args)
elif args.input_style == "B":
fake = model.network_G(input, name="GB", cfg=args)
else:
raise "Input with style [%s] is not supported." % args.input_style
elif args.model_net == 'Pix2pix':
from network.Pix2pix_network import Pix2pix_model
model = Pix2pix_model()
fake = model.network_G(input, "generator", cfg=args)
elif args.model_net == 'StarGAN':
from network.StarGAN_network import StarGAN_model
model = StarGAN_model()
fake = model.network_G(input, label_trg_, name="g_main", cfg=args)
elif args.model_net == 'STGAN':
from network.STGAN_network import STGAN_model
model = STGAN_model()
fake, _ = model.network_G(
input,
label_org_,
label_trg_,
cfg=args,
name='generator',
is_test=True)
elif args.model_net == 'AttGAN':
from network.AttGAN_network import AttGAN_model
model = AttGAN_model()
fake, _ = model.network_G(
input,
label_org_,
label_trg_,
cfg=args,
name='generator',
is_test=True)
elif args.model_net == 'CGAN':
noise = fluid.layers.data(
name='noise', shape=[args.noise_size], dtype='float32')
conditions = fluid.layers.data(
name='conditions', shape=[1], dtype='float32')
from network.CGAN_network import CGAN_model
model = CGAN_model()
fake = model.network_G(noise, conditions, name="G")
elif args.model_net == 'DCGAN':
noise = fluid.layers.data(
name='noise', shape=[args.noise_size], dtype='float32')
from network.DCGAN_network import DCGAN_model
model = DCGAN_model()
fake = model.network_G(noise, name="G")
else:
raise NotImplementedError("model_net {} is not support".format(
args.model_net))
# prepare environment
place = fluid.CPUPlace()
if args.use_gpu:
place = fluid.CUDAPlace(0)
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
for var in fluid.default_main_program().global_block().all_parameters():
print(var.name)
print(args.init_model + '/' + model_name)
fluid.io.load_persistables(exe, args.init_model + "/" + model_name)
print('load params done')
if not os.path.exists(args.output):
os.makedirs(args.output)
attr_names = args.selected_attrs.split(',')
if args.model_net == 'AttGAN' or args.model_net == 'STGAN':
test_reader = celeba_reader_creator(
image_dir=args.dataset_dir,
list_filename=args.test_list,
batch_size=args.batch_size,
drop_last=False,
args=args)
reader_test = test_reader.get_test_reader(
args, shuffle=False, return_name=True)
for data in zip(reader_test()):
real_img, label_org, name = data[0]
print("read {}".format(name))
label_trg = copy.deepcopy(label_org)
tensor_img = fluid.LoDTensor()
tensor_label_org = fluid.LoDTensor()
tensor_label_trg = fluid.LoDTensor()
tensor_label_org_ = fluid.LoDTensor()
tensor_label_trg_ = fluid.LoDTensor()
tensor_img.set(real_img, place)
tensor_label_org.set(label_org, place)
real_img_temp = save_batch_image(real_img)
images = [real_img_temp]
for i in range(args.c_dim):
label_trg_tmp = copy.deepcopy(label_trg)
for j in range(len(label_org)):
label_trg_tmp[j][i] = 1.0 - label_trg_tmp[j][i]
label_trg_tmp = check_attribute_conflict(
label_trg_tmp, attr_names[i], attr_names)
label_org_ = list(map(lambda x: ((x * 2) - 1) * 0.5, label_org))
label_trg_ = list(
map(lambda x: ((x * 2) - 1) * 0.5, label_trg_tmp))
if args.model_net == 'AttGAN':
for k in range(len(label_org)):
label_trg_[k][i] = label_trg_[k][i] * 2.0
tensor_label_org_.set(label_org_, place)
tensor_label_trg.set(label_trg, place)
tensor_label_trg_.set(label_trg_, place)
out = exe.run(feed={
"input": tensor_img,
"label_org_": tensor_label_org_,
"label_trg_": tensor_label_trg_
},
fetch_list=[fake.name])
fake_temp = save_batch_image(out[0])
images.append(fake_temp)
images_concat = np.concatenate(images, 1)
if len(label_org) > 1:
images_concat = np.concatenate(images_concat, 1)
imageio.imwrite(args.output + "/fake_img_" + name[0], (
(images_concat + 1) * 127.5).astype(np.uint8))
elif args.model_net == 'StarGAN':
test_reader = celeba_reader_creator(
image_dir=args.dataset_dir,
list_filename=args.test_list,
batch_size=args.batch_size,
drop_last=False,
args=args)
reader_test = test_reader.get_test_reader(
args, shuffle=False, return_name=True)
for data in zip(reader_test()):
real_img, label_org, name = data[0]
print("read {}".format(name))
tensor_img = fluid.LoDTensor()
tensor_label_org = fluid.LoDTensor()
tensor_img.set(real_img, place)
tensor_label_org.set(label_org, place)
real_img_temp = save_batch_image(real_img)
images = [real_img_temp]
for i in range(args.c_dim):
label_trg_tmp = copy.deepcopy(label_org)
for j in range(len(label_org)):
label_trg_tmp[j][i] = 1.0 - label_trg_tmp[j][i]
label_trg = check_attribute_conflict(
label_trg_tmp, attr_names[i], attr_names)
tensor_label_trg = fluid.LoDTensor()
tensor_label_trg.set(label_trg, place)
out = exe.run(
feed={"input": tensor_img,
"label_trg_": tensor_label_trg},
fetch_list=[fake.name])
fake_temp = save_batch_image(out[0])
images.append(fake_temp)
images_concat = np.concatenate(images, 1)
if len(label_org) > 1:
images_concat = np.concatenate(images_concat, 1)
imageio.imwrite(args.output + "/fake_img_" + name[0], (
(images_concat + 1) * 127.5).astype(np.uint8))
elif args.model_net == 'Pix2pix' or args.model_net == 'CycleGAN':
for file in glob.glob(args.dataset_dir):
print("read {}".format(file))
image_name = os.path.basename(file)
image = Image.open(file).convert('RGB')
image = image.resize((256, 256), Image.BICUBIC)
image = np.array(image).transpose([2, 0, 1]).astype('float32')
image = image / 255.0
image = (image - 0.5) / 0.5
data = image[np.newaxis, :]
tensor = fluid.LoDTensor()
tensor.set(data, place)
fake_temp = exe.run(fetch_list=[fake.name], feed={"input": tensor})
fake_temp = np.squeeze(fake_temp[0]).transpose([1, 2, 0])
input_temp = np.squeeze(data).transpose([1, 2, 0])
imageio.imwrite(args.output + "/fake_" + image_name, (
(fake_temp + 1) * 127.5).astype(np.uint8))
elif args.model_net == 'CGAN':
noise_data = np.random.uniform(
low=-1.0, high=1.0,
size=[args.batch_size, args.noise_size]).astype('float32')
label = np.random.randint(
0, 9, size=[args.batch_size, 1]).astype('float32')
noise_tensor = fluid.LoDTensor()
conditions_tensor = fluid.LoDTensor()
noise_tensor.set(noise_data, place)
conditions_tensor.set(label, place)
fake_temp = exe.run(
fetch_list=[fake.name],
feed={"noise": noise_tensor,
"conditions": conditions_tensor})[0]
fake_image = np.reshape(fake_temp, (args.batch_size, -1))
fig = utility.plot(fake_image)
plt.savefig(args.output + '/fake_cgan.png', bbox_inches='tight')
plt.close(fig)
elif args.model_net == 'DCGAN':
noise_data = np.random.uniform(
low=-1.0, high=1.0,
size=[args.batch_size, args.noise_size]).astype('float32')
noise_tensor = fluid.LoDTensor()
noise_tensor.set(noise_data, place)
fake_temp = exe.run(fetch_list=[fake.name],
feed={"noise": noise_tensor})[0]
fake_image = np.reshape(fake_temp, (args.batch_size, -1))
fig = utility.plot(fake_image)
plt.savefig(args.output + '/fake_dcgan.png', bbox_inches='tight')
plt.close(fig)
else:
raise NotImplementedError("model_net {} is not support".format(
args.model_net))
def __init__(self, image_real, label_org, label_trg, cfg, step_per_epoch):
self.program = fluid.default_main_program().clone()
with fluid.program_guard(self.program):
model = StarGAN_model()
image_real = fluid.data(name='image_real',
shape=image_real.shape,
dtype='float32')
self.fake_img = model.network_G(image_real,
label_trg,
cfg,
name="g_main")
self.pred_real, self.cls_real = model.network_D(image_real,
cfg,
name="d_main")
self.pred_fake, _ = model.network_D(self.fake_img,
cfg,
name="d_main")
cls_shape = self.cls_real.shape
self.cls_real = fluid.layers.reshape(
self.cls_real,
[-1, cls_shape[1] * cls_shape[2] * cls_shape[3]])
self.d_loss_cls = fluid.layers.reduce_sum(
fluid.layers.sigmoid_cross_entropy_with_logits(
self.cls_real, label_org)) / cfg.batch_size
if cfg.gan_mode != 'wgan':
raise NotImplementedError(
"gan_mode {} is not support! only support wgan".format(
cfg.gan_mode))
#wgan
self.d_loss_fake = fluid.layers.mean(self.pred_fake)
self.d_loss_real = -1 * fluid.layers.mean(self.pred_real)
self.d_loss_gp = self.gradient_penalty(getattr(model, "network_D"),
image_real,
self.fake_img,
cfg=cfg,
name="d_main")
self.d_loss = self.d_loss_real + self.d_loss_fake + self.d_loss_cls + cfg.lambda_gp * self.d_loss_gp
vars = []
for var in self.program.list_vars():
if fluid.io.is_parameter(var) and var.name.startswith("d_"):
vars.append(var.name)
self.param = vars
total_iters = step_per_epoch * cfg.epoch
boundaries = [cfg.num_iters - cfg.num_iters_decay]
values = [cfg.d_lr]
lr = cfg.d_lr
for x in range(cfg.num_iters - cfg.num_iters_decay + 1,
total_iters):
if x % cfg.lr_update_step == 0:
boundaries.append(x)
lr -= (lr / float(cfg.num_iters_decay))
values.append(lr)
lr = values[-1]
lr -= (lr / float(cfg.num_iters_decay))
values.append(lr)
optimizer = fluid.optimizer.Adam(
learning_rate=fluid.layers.piecewise_decay(
boundaries=boundaries, values=values),
beta1=0.5,
beta2=0.999,
name="net_D")
optimizer.minimize(self.d_loss, parameter_list=vars)