# = graphs =
# ==============================================================================
# data
if threads >= 0:
cpu_config = tf.ConfigProto(intra_op_parallelism_threads=threads // 2,
inter_op_parallelism_threads=threads // 2,
device_count={'CPU': threads})
sess = tf.Session(config=cpu_config)
else:
sess = tl.session()
crop_ = not use_cropped_img
tr_data = data.Celeba(dataroot,
atts,
img_size,
batch_size,
part='train',
sess=sess,
crop=crop_)
val_data = data.Celeba(dataroot,
atts,
img_size,
n_sample,
part='val',
shuffle=False,
sess=sess,
crop=crop_)
# models
Genc = partial(models.Genc,
dim=enc_dim,
test_int_min = args_.test_int_min
test_int_max = args_.test_int_max
n_slide = args_.n_slide
# others
experiment_name = args_.experiment_name
assert test_att is not None, 'test_att should be chosen in %s' % (str(atts))
# ==============================================================================
# = graphs =
# ==============================================================================
# data
sess = tl.session()
te_data = data.Celeba('./data', atts, img_size, 1, part='test', sess=sess)
# models
Genc = partial(models.Genc, dim=enc_dim, n_layers=enc_layers)
Gdec = partial(models.Gdec, dim=dec_dim, n_layers=dec_layers, shortcut_layers=shortcut_layers, inject_layers=inject_layers)
# inputs
xa_sample = tf.placeholder(tf.float32, shape=[None, img_size, img_size, 3])
_b_sample = tf.placeholder(tf.float32, shape=[None, n_att])
# sample
x_sample = Gdec(Genc(xa_sample, is_training=False), _b_sample, is_training=False)
# ==============================================================================
# = test =
experiment_name = args.experiment_name
pylib.mkdir('./output/%s' % experiment_name)
with open('./output/%s/setting.txt' % experiment_name, 'w') as f:
f.write(json.dumps(vars(args), indent=4, separators=(',', ':')))
# ==============================================================================
# = graphs =
# ==============================================================================
# data
sess = tl.session()
tr_data = data.Celeba('./data',
atts,
img_size,
batch_size,
part='train',
sess=sess,
crop=not use_cropped_img)
val_data = data.Celeba('./data',
atts,
img_size,
n_sample,
part='val',
shuffle=False,
sess=sess,
crop=not use_cropped_img)
# models
Genc = partial(models.Genc, dim=enc_dim, n_layers=enc_layers)
Gdec = partial(models.Gdec,
experiment_name = args_.experiment_name
assert test_atts is not None, 'test_atts should be chosen in %s' % (str(atts))
for a in test_atts:
assert a in atts, 'test_atts should be chosen in %s' % (str(atts))
assert len(test_ints) == len(test_atts), 'the lengths of test_ints and test_atts should be the same!'
# ==============================================================================
# = graphs =
# ==============================================================================
# data
sess = tl.session()
te_data = data.Celeba('./data', atts, img_size, 1, part='test', sess=sess, crop=not use_cropped_img)
# models
Genc = partial(models.Genc, dim=enc_dim, n_layers=enc_layers)
Gdec = partial(models.Gdec, dim=dec_dim, n_layers=dec_layers, shortcut_layers=shortcut_layers,
inject_layers=inject_layers)
# inputs
xa_sample = tf.placeholder(tf.float32, shape=[None, img_size, img_size, 3])
_b_sample = tf.placeholder(tf.float32, shape=[None, n_att])
# sample
x_sample = Gdec(Genc(xa_sample, is_training=False), _b_sample, is_training=False)
# ==============================================================================
use_cropped_img = args['use_cropped_img']
experiment_name = args_.experiment_name
# ===========================================================================
# graphs
# ===========================================================================
# os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
# dataset
sess = tl.session()
te_data = data.Celeba(args_.datadir,
args_.test_att_list,
atts,
img_size,
batch_size=1,
part='test',
sess=sess,
crop=not use_cropped_img,
im_no=img)
# models
Genc = partial(models.Genc,
dim=enc_dim,
n_layers=enc_layers,
multi_inputs=multi_inputs)
Gdec = partial(models.Gdec,
dim=dec_dim,
n_layers=dec_layers,
shortcut_layers=shortcut_layers,
inject_layers=inject_layers,
one_more_conv=one_more_conv)
thres_int = args['thres_int']
# others
use_cropped_img = args['use_cropped_img']
experiment_name = args_.experiment_name
# ==============================================================================
# = graphs =
# ==============================================================================
# data
sess = tl.session()
te_data = data.Celeba(dataroot,
atts,
img_size,
1,
part='test',
sess=sess,
crop=not use_cropped_img,
im_no=img)
# models
Genc = partial(models.Genc,
dim=enc_dim,
n_layers=enc_layers,
multi_inputs=multi_inputs)
Gdec = partial(models.Gdec,
dim=dec_dim,
n_layers=dec_layers,
shortcut_layers=shortcut_layers,
inject_layers=inject_layers,
one_more_conv=one_more_conv)
Gstu = partial(models.Gstu,
z_dim = args.z_dim
loss_mode = args.loss_mode
gp_mode = args.gp_mode
norm = args.norm
experiment_name = args.experiment_name
pylib.mkdir('./output/%s' % experiment_name)
with open('./output/%s/setting.txt' % experiment_name, 'w') as f:
f.write(json.dumps(vars(args), indent=4, separators=(',', ':')))
img_size = 64
# dataset
dataset = data.Celeba('./data', ['Bangs' if att == '' else att], img_size,
batch_size)
# ==============================================================================
# = graph =
# ==============================================================================
# models
c_dim = len(model.sample_c(ks)[0])
D = partial(model.D, c_dim=c_dim, norm_name=norm)
G = model.G
# otpimizer
if optimizer == 'adam':
optim = partial(tf.train.AdamOptimizer, beta1=0.5)
elif optimizer == 'rmsprop':
optim = tf.train.RMSPropOptimizer
# others
experiment_name = args.experiment_name
pylib.mkdir('./output/%s' % experiment_name)
with open('./output/%s/setting.txt' % experiment_name, 'w') as f:
f.write(json.dumps(vars(args), indent=4, separators=(',', ':')))
# ==============================================================================
# = graphs =
# ==============================================================================
# data
sess = tl.session()
tr_data = data.Celeba('./data',
atts,
img_size,
batch_size,
part='train',
sess=sess)
val_data = data.Celeba('./data',
atts,
img_size,
n_sample,
part='val',
shuffle=False,
sess=sess)
# models
Genc = partial(models.Genc, dim=enc_dim, n_layers=enc_layers)
Gdec = partial(models.Gdec,
dim=dec_dim,
n_layers=dec_layers,
def runModel(image_url, file_name, test_att, n_slide, image_labels,
model_type):
# ==============================================================================
# = param =
# ==============================================================================
parser = argparse.ArgumentParser()
parser.add_argument('--experiment_name',
dest='experiment_name',
default="384_shortcut1_inject1_none_hd",
help='experiment_name')
parser.add_argument('--test_att', dest='test_att', help='test_att')
parser.add_argument('--test_int_min',
dest='test_int_min',
type=float,
default=-1.0,
help='test_int_min')
parser.add_argument('--test_int_max',
dest='test_int_max',
type=float,
default=1.0,
help='test_int_max')
args_ = parser.parse_args()
if model_type == 0:
experiment_name = args_.experiment_name
else:
experiment_name = "128_custom"
print("EXPERIMENT NAME WORKING:" + experiment_name)
with open('./output/%s/setting.txt' % experiment_name) as f:
args = json.load(f)
# model
atts = args['atts']
n_att = len(atts)
img_size = args['img_size']
shortcut_layers = args['shortcut_layers']
inject_layers = args['inject_layers']
enc_dim = args['enc_dim']
dec_dim = args['dec_dim']
dis_dim = args['dis_dim']
dis_fc_dim = args['dis_fc_dim']
enc_layers = args['enc_layers']
dec_layers = args['dec_layers']
dis_layers = args['dis_layers']
# testing
thres_int = args['thres_int']
test_int_min = args_.test_int_min
test_int_max = args_.test_int_max
# others
use_cropped_img = args['use_cropped_img']
n_slide = int(n_slide)
assert test_att is not None, 'test_att should be chosen in %s' % (
str(atts))
# ==============================================================================
# = graphs =
# ==============================================================================
# data
sess = tl.session()
# get image
print(image_url)
if experiment_name == "128_custom":
os.system(
"wget -P /home/tug44606/AttGAN-Tensorflow-master/data/img_align_celeba "
+ image_url)
else:
os.system(
"wget -P /home/tug44606/AttGAN-Tensorflow-master/data/img_crop_celeba "
+ image_url)
print("Working")
# pass image with labels to dataset
te_data = data.Celeba('./data',
atts,
img_size,
1,
part='val',
sess=sess,
crop=not use_cropped_img,
image_labels=image_labels,
file_name=file_name)
sample = None
# models
Genc = partial(models.Genc, dim=enc_dim, n_layers=enc_layers)
Gdec = partial(models.Gdec,
dim=dec_dim,
n_layers=dec_layers,
shortcut_layers=shortcut_layers,
inject_layers=inject_layers)
# inputs
xa_sample = tf.placeholder(tf.float32, shape=[None, img_size, img_size, 3])
_b_sample = tf.placeholder(tf.float32, shape=[None, n_att])
# sample
x_sample = Gdec(Genc(xa_sample, is_training=False),
_b_sample,
is_training=False)
# ==============================================================================
# = test =
# ==============================================================================
# initialization
ckpt_dir = './output/%s/checkpoints' % experiment_name
print("CHECKPOINT DIR: " + ckpt_dir)
try:
tl.load_checkpoint(ckpt_dir, sess)
except:
raise Exception(' [*] No checkpoint!')
save_location = ""
# sample
try:
for idx, batch in enumerate(te_data):
xa_sample_ipt = batch[0]
b_sample_ipt = batch[1]
x_sample_opt_list = []
for i in range(n_slide - 1, n_slide):
test_int = (test_int_max -
test_int_min) / (n_slide - 1) * i + test_int_min
_b_sample_ipt = (b_sample_ipt * 2 - 1) * thres_int
_b_sample_ipt[..., atts.index(test_att)] = test_int
x_sample_opt_list.append(
sess.run(x_sample,
feed_dict={
xa_sample: xa_sample_ipt,
_b_sample: _b_sample_ipt
}))
sample = np.concatenate(x_sample_opt_list, 2)
save_location = '/output/%s/sample_testing_slide_%s/' % (
experiment_name, test_att)
save_dir = './output/%s/sample_testing_slide_%s' % (
experiment_name, test_att)
pylib.mkdir(save_dir)
im.imwrite(sample.squeeze(0), '%s/%s' % (save_dir, file_name))
print('%d.png done!' % (idx + 0))
if (idx + 1 == te_data._img_num):
break
except:
traceback.print_exc()
finally:
sess.close()
if experiment_name == "128_custom":
os.system("rm ./data/img_align_celeba/" + file_name)
else:
os.system("rm ./data/img_crop_celeba/" + file_name)
return "http://129.32.22.10:7001" + save_location + file_name
