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test_slide.py
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import os
import imlib as im
import numpy as np
import pylib as py
import tensorflow as tf
import tflib as tl
import tqdm
import data
import module
# ==============================================================================
# = param =
# ==============================================================================
py.arg('--img_dir', default='./data/img_celeba/aligned/align_size(572,572)_move(0.250,0.000)_face_factor(0.450)_jpg/data')
py.arg('--test_label_path', default='./data/img_celeba/test_label.txt')
py.arg('--test_att_name', choices=data.ATT_ID.keys(), default='Pale_Skin')
py.arg('--test_int_min', type=float, default=-2)
py.arg('--test_int_max', type=float, default=2)
py.arg('--test_int_step', type=float, default=0.5)
py.arg('--experiment_name', default='default')
args_ = py.args()
# output_dir
output_dir = py.join('output', args_.experiment_name)
# save settings
args = py.args_from_yaml(py.join(output_dir, 'settings.yml'))
args.__dict__.update(args_.__dict__)
# others
n_atts = len(args.att_names)
sess = tl.session()
sess.__enter__() # make default
# ==============================================================================
# = data and model =
# ==============================================================================
# data
test_dataset, len_test_dataset = data.make_celeba_dataset(args.img_dir, args.test_label_path, args.att_names, args.n_samples,
load_size=args.load_size, crop_size=args.crop_size,
training=False, drop_remainder=False, shuffle=False, repeat=None)
test_iter = test_dataset.make_one_shot_iterator()
# ==============================================================================
# = graph =
# ==============================================================================
def sample_graph():
# ======================================
# = graph =
# ======================================
test_next = test_iter.get_next()
if not os.path.exists(py.join(output_dir, 'generator.pb')):
# model
Genc, Gdec, _ = module.get_model(args.model, n_atts, weight_decay=args.weight_decay)
# placeholders & inputs
xa = tf.placeholder(tf.float32, shape=[None, args.crop_size, args.crop_size, 3])
b_ = tf.placeholder(tf.float32, shape=[None, n_atts])
# sample graph
x = Gdec(Genc(xa, training=False), b_, training=False)
else:
# load freezed model
with tf.gfile.GFile(py.join(output_dir, 'generator.pb'), 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
tf.import_graph_def(graph_def, name='generator')
# placeholders & inputs
xa = sess.graph.get_tensor_by_name('generator/xa:0')
b_ = sess.graph.get_tensor_by_name('generator/b_:0')
# sample graph
x = sess.graph.get_tensor_by_name('generator/xb:0')
# ======================================
# = run function =
# ======================================
save_dir = './output/%s/samples_testing_slide/%s_%s_%s_%s' % \
(args.experiment_name, args.test_att_name, '{:g}'.format(args.test_int_min), '{:g}'.format(args.test_int_max), '{:g}'.format(args.test_int_step))
py.mkdir(save_dir)
def run():
cnt = 0
for _ in tqdm.trange(len_test_dataset):
# data for sampling
xa_ipt, a_ipt = sess.run(test_next)
b_ipt = np.copy(a_ipt)
b__ipt = (b_ipt * 2 - 1).astype(np.float32) # !!!
x_opt_list = [xa_ipt]
for test_int in np.arange(args.test_int_min, args.test_int_max + 1e-5, args.test_int_step):
b__ipt[:, args.att_names.index(args.test_att_name)] = test_int
x_opt = sess.run(x, feed_dict={xa: xa_ipt, b_: b__ipt})
x_opt_list.append(x_opt)
sample = np.transpose(x_opt_list, (1, 2, 0, 3, 4))
sample = np.reshape(sample, (sample.shape[0], -1, sample.shape[2] * sample.shape[3], sample.shape[4]))
for s in sample:
cnt += 1
im.imwrite(s, '%s/%d.jpg' % (save_dir, cnt))
return run
sample = sample_graph()
# ==============================================================================
# = test =
# ==============================================================================
# checkpoint
if not os.path.exists(py.join(output_dir, 'generator.pb')):
checkpoint = tl.Checkpoint(
{v.name: v for v in tf.global_variables()},
py.join(output_dir, 'checkpoints'),
max_to_keep=1
)
checkpoint.restore().run_restore_ops()
sample()
sess.close()