def _get_inference_data():
# Setup global tensorflow state
sess = _setup_tensorflow()
# Load single image to use for inference
if FLAGS.infile is None:
raise ValueError(
'Must specify inference input file through `--infile <filename>` command line argument'
)
if not tf.gfile.Exists(FLAGS.infile) or tf.gfile.IsDirectory(FLAGS.infile):
raise FileNotFoundError('File `%s` does not exist or is a directory' %
(FLAGS.infile, ))
filenames = [FLAGS.infile]
infer_images = dm_input.input_data(sess, 'inference', filenames)
print('Loading model...')
# Create inference model
infer_model = dm_model.create_model(sess, infer_images)
# Load model parameters from checkpoint
checkpoint = tf.train.get_checkpoint_state(FLAGS.checkpoint_dir)
try:
saver = tf.train.Saver()
saver.restore(sess, checkpoint.model_checkpoint_path)
del saver
del checkpoint
except:
raise RuntimeError('Unable to read checkpoint from `%s`' %
(FLAGS.checkpoint_dir, ))
print('Done.')
# Pack all for convenience
infer_data = dm_utils.Container(locals())
return infer_data
def _get_train_data(random_test_sample=True):
# Setup global tensorflow state
sess = _setup_tensorflow()
# Prepare directories
_prepare_train_dirs()
# Which type of transformation?
# Note: eyeglasses and sunglasses are filtered out because they tend to produce artifacts
if FLAGS.train_mode == 'ftm' or FLAGS.train_mode == 'f2m':
# Trans filter: from female to attractive male
# Note: removed facial hair from target images because otherwise the network becomes overly focused on rendering facial hair
# Note: also removed objects such as glasses and hats to avoid rendering these low gender influenced objects
source_filter = {
'Male': False,
'Blurry': False,
'Eyeglasses': False,
'Attractive': True,
'Eyeglasses': False,
'Wearing_Hat': False,
'Goatee': False,
'Mustache': False,
'No_Beard': True
}
target_filter = {
'Male': True,
'Blurry': False,
'Eyeglasses': False,
'Attractive': True,
'Eyeglasses': False,
'Wearing_Hat': False,
'Goatee': False,
'Mustache': False,
'No_Beard': True
}
elif FLAGS.train_mode == 'mtf' or FLAGS.train_mode == 'm2f':
# Trans filter: from male to attractuve female
source_filter = {
'Male': True,
'Blurry': False,
'Eyeglasses': False,
'Attractive': True,
'Eyeglasses': False,
'Wearing_Hat': False,
'Goatee': False,
'Mustache': False,
'No_Beard': True
}
target_filter = {
'Male': False,
'Blurry': False,
'Eyeglasses': False,
'Attractive': True,
'Eyeglasses': False,
'Wearing_Hat': False,
'Goatee': False,
'Mustache': False,
'No_Beard': True
}
elif FLAGS.train_mode == 'ftf' or FLAGS.train_mode == 'f2f':
# Vanity filter: from female to attractive female
source_filter = {
'Male': False,
'Blurry': False,
'Eyeglasses': False,
'Attractive': True,
'Eyeglasses': False,
'Wearing_Hat': False,
'Goatee': False,
'Mustache': False,
'No_Beard': True
}
target_filter = {
'Male': False,
'Blurry': False,
'Eyeglasses': False,
'Attractive': True,
'Eyeglasses': False,
'Wearing_Hat': False,
'Goatee': False,
'Mustache': False,
'No_Beard': True
}
elif FLAGS.train_mode == "mtm" or FLAGS.train_mode == 'm2m':
# Vanity filter: from male to attractive male
source_filter = {
'Male': True,
'Blurry': False,
'Eyeglasses': False,
'Attractive': True,
'Eyeglasses': False,
'Wearing_Hat': False,
'Goatee': False,
'Mustache': False,
'No_Beard': True
}
target_filter = {
'Male': True,
'Blurry': False,
'Eyeglasses': False,
'Attractive': True,
'Eyeglasses': False,
'Wearing_Hat': False,
'Goatee': False,
'Mustache': False,
'No_Beard': True
}
else:
raise ValueError(
'`train_mode` must be one of: `ftm`, `mtf`, `ftf` or `mtm`')
# Setup async input queues
selected = dm_celeba.select_samples(source_filter)
source_images = dm_input.input_data(sess, 'train', selected)
if random_test_sample:
test_images = dm_input.input_data(sess, 'test', selected)
else:
test_selected = dm_celeba.select_samples(source_filter,
random_sample=False)
test_images = dm_input.input_data(sess, 'test', test_selected)
print('%8d source images selected' % (len(selected), ))
selected = dm_celeba.select_samples(target_filter)
target_images = dm_input.input_data(sess, 'train', selected)
print('%8d target images selected' % (len(selected), ))
print()
# Annealing temperature: starts at 1.0 and decreases exponentially over time
annealing = tf.Variable(initial_value=1.0,
trainable=False,
name='annealing')
halve_annealing = tf.assign(annealing, 0.5 * annealing)
print('Using gan type: %s', specifiedgan)
# Create and initialize training and testing models
train_model = dm_model.create_model(sess,
source_images,
target_images,
annealing,
verbose=True,
gan_type=specifiedgan)
print("Building testing model...")
test_model = dm_model.create_model(sess,
test_images,
None,
annealing,
gan_type=specifiedgan)
print("Done.")
# Forget this line and TF will deadlock at the beginning of training
tf.train.start_queue_runners(sess=sess)
# Pack all for convenience
train_data = dm_utils.Container(locals())
return train_data