def save_data(self, x, name, save_dir, x_is_real_x=False):
"""Save dataspace instances.
Args:
x: A numpy array of dataspace points.
name: A string indicating the name in the saved file.
save_dir: A string indicating the directory to put the saved file.
x_is_real_x: An boolean indicating whether `x` is already in dataspace. If
not, `x` is converted to dataspace before saving
"""
real_x = x if x_is_real_x else self.decode(x)
real_x = common.post_proc(real_x, self.config)
batched_real_x = common.batch_image(real_x)
sample_file = os.path.join(save_dir, '%s.png' % name)
common.save_image(batched_real_x, sample_file)
def save_data(self, x, name, save_dir, x_is_real_x=False):
"""Save dataspace instances.
Args:
x: A numpy array of dataspace points.
name: A string indicating the name in the saved file.
save_dir: A string indicating the directory to put the saved file.
x_is_real_x: An boolean indicating whether `x` is already in dataspace. If
not, `x` is converted to dataspace before saving
"""
real_x = x if x_is_real_x else self.decode(x)
real_x = common.post_proc(real_x, self.config)
batched_real_x = common.batch_image(real_x)
sample_file = os.path.join(save_dir, '%s.png' % name)
common.save_image(batched_real_x, sample_file)
def main(unused_argv):
# pylint:disable=unused-variable
# Reason:
# This training script relys on many programmatical call to function and
# access to variables. Pylint cannot infer this case so it emits false alarm
# of unused-variable if we do not disable this warning.
# pylint:disable=invalid-name
# Reason:
# Following variables have their name consider to be invalid by pylint so
# we disable the warning.
# - Variable that in its name has A or B indicating their belonging of
# one side of data.
del unused_argv
# Load main config
config_name = FLAGS.config
config = load_config(config_name)
config_name_A = config['config_A']
config_name_B = config['config_B']
config_name_classifier_A = config['config_classifier_A']
config_name_classifier_B = config['config_classifier_B']
# Load dataset
dataset_A = common_joint.load_dataset(config_name_A, FLAGS.exp_uid_A)
(dataset_blob_A, train_data_A, train_label_A, train_mu_A, train_sigma_A,
index_grouped_by_label_A) = dataset_A
dataset_B = common_joint.load_dataset(config_name_B, FLAGS.exp_uid_B)
(dataset_blob_B, train_data_B, train_label_B, train_mu_B, train_sigma_B,
index_grouped_by_label_B) = dataset_B
# Prepare directories
dirs = common_joint.prepare_dirs('joint', config_name, FLAGS.exp_uid)
save_dir, sample_dir = dirs
# Set random seed
np.random.seed(FLAGS.random_seed)
tf.set_random_seed(FLAGS.random_seed)
# Real Training.
tf.reset_default_graph()
sess = tf.Session()
# Load model's architecture (= build)
one_side_helper_A = common_joint.OneSideHelper(config_name_A, FLAGS.exp_uid_A,
config_name_classifier_A,
FLAGS.exp_uid_classifier)
one_side_helper_B = common_joint.OneSideHelper(config_name_B, FLAGS.exp_uid_B,
config_name_classifier_B,
FLAGS.exp_uid_classifier)
m = common_joint.load_model(model_joint.Model, config_name, FLAGS.exp_uid)
# Initialize and restore
sess.run(tf.global_variables_initializer())
one_side_helper_A.restore(dataset_blob_A)
one_side_helper_B.restore(dataset_blob_B)
# Restore from ckpt
config_name = FLAGS.config
model_uid = common.get_model_uid(config_name, FLAGS.exp_uid)
save_name = os.path.join(
save_dir, 'transfer_%s_%d.ckpt' % (model_uid, FLAGS.load_ckpt_iter))
m.vae_saver.restore(sess, save_name)
# prepare intepolate dir
intepolate_dir = os.path.join(
sample_dir, 'interpolate_sample', '%010d' % FLAGS.load_ckpt_iter)
tf.gfile.MakeDirs(intepolate_dir)
# things
interpolate_labels = [int(_) for _ in FLAGS.interpolate_labels.split(',')]
nb_images_between_labels = FLAGS.nb_images_between_labels
index_list_A = []
last_pos = [0] * 10
for label in interpolate_labels:
index_list_A.append(index_grouped_by_label_A[label][last_pos[label]])
last_pos[label] += 1
index_list_B = []
last_pos = [-1] * 10
for label in interpolate_labels:
index_list_B.append(index_grouped_by_label_B[label][last_pos[label]])
last_pos[label] -= 1
z_A = []
z_A.append(train_mu_A[index_list_A[0]])
for i_label in range(1, len(interpolate_labels)):
last_z_A = z_A[-1]
this_z_A = train_mu_A[index_list_A[i_label]]
for j in range(1, nb_images_between_labels + 1):
z_A.append(last_z_A +
(this_z_A - last_z_A) * (float(j) / nb_images_between_labels))
z_B = []
z_B.append(train_mu_B[index_list_B[0]])
for i_label in range(1, len(interpolate_labels)):
last_z_B = z_B[-1]
this_z_B = train_mu_B[index_list_B[i_label]]
for j in range(1, nb_images_between_labels + 1):
z_B.append(last_z_B +
(this_z_B - last_z_B) * (float(j) / nb_images_between_labels))
z_B_tr = []
for this_z_A in z_A:
this_z_B_tr = sess.run(m.x_A_to_B_direct, {m.x_A: np.array([this_z_A])})
z_B_tr.append(this_z_B_tr[0])
# Generate data domain instances and save.
z_A = np.array(z_A)
x_A = one_side_helper_A.m_helper.decode(z_A)
x_A = common.post_proc(x_A, one_side_helper_A.m_helper.config)
batched_x_A = common.batch_image(
x_A,
max_images=len(x_A),
rows=len(x_A),
cols=1,
)
common.save_image(batched_x_A, os.path.join(intepolate_dir, 'x_A.png'))
z_B = np.array(z_B)
x_B = one_side_helper_B.m_helper.decode(z_B)
x_B = common.post_proc(x_B, one_side_helper_B.m_helper.config)
batched_x_B = common.batch_image(
x_B,
max_images=len(x_B),
rows=len(x_B),
cols=1,
)
common.save_image(batched_x_B, os.path.join(intepolate_dir, 'x_B.png'))
z_B_tr = np.array(z_B_tr)
x_B_tr = one_side_helper_B.m_helper.decode(z_B_tr)
x_B_tr = common.post_proc(x_B_tr, one_side_helper_B.m_helper.config)
batched_x_B_tr = common.batch_image(
x_B_tr,
max_images=len(x_B_tr),
rows=len(x_B_tr),
cols=1,
)
common.save_image(batched_x_B_tr, os.path.join(intepolate_dir, 'x_B_tr.png'))
def main(unused_argv):
# pylint:disable=unused-variable
# Reason:
# This training script relys on many programmatical call to function and
# access to variables. Pylint cannot infer this case so it emits false alarm
# of unused-variable if we do not disable this warning.
# pylint:disable=invalid-name
# Reason:
# Following variables have their name consider to be invalid by pylint so
# we disable the warning.
# - Variable that in its name has A or B indictating their belonging of
# one side of data.
del unused_argv
# Load main config
config_name = FLAGS.config
config = load_config(config_name)
config_name_A = config['config_A']
config_name_B = config['config_B']
config_name_classifier_A = config['config_classifier_A']
config_name_classifier_B = config['config_classifier_B']
# Load dataset
dataset_A = common_joint.load_dataset(config_name_A, FLAGS.exp_uid_A)
(dataset_blob_A, train_data_A, train_label_A, train_mu_A, train_sigma_A,
index_grouped_by_label_A) = dataset_A
dataset_B = common_joint.load_dataset(config_name_B, FLAGS.exp_uid_B)
(dataset_blob_B, train_data_B, train_label_B, train_mu_B, train_sigma_B,
index_grouped_by_label_B) = dataset_B
# Prepare directories
dirs = common_joint.prepare_dirs('joint', config_name, FLAGS.exp_uid)
save_dir, sample_dir = dirs
# Set random seed
np.random.seed(FLAGS.random_seed)
tf.set_random_seed(FLAGS.random_seed)
# Real Training.
tf.reset_default_graph()
sess = tf.Session()
# Load model's architecture (= build)
one_side_helper_A = common_joint.OneSideHelper(config_name_A, FLAGS.exp_uid_A,
config_name_classifier_A,
FLAGS.exp_uid_classifier)
one_side_helper_B = common_joint.OneSideHelper(config_name_B, FLAGS.exp_uid_B,
config_name_classifier_B,
FLAGS.exp_uid_classifier)
m = common_joint.load_model(model_joint.Model, config_name, FLAGS.exp_uid)
# Initialize and restore
sess.run(tf.global_variables_initializer())
one_side_helper_A.restore(dataset_blob_A)
one_side_helper_B.restore(dataset_blob_B)
# Restore from ckpt
config_name = FLAGS.config
model_uid = common.get_model_uid(config_name, FLAGS.exp_uid)
save_name = join(save_dir,
'transfer_%s_%d.ckpt' % (model_uid, FLAGS.load_ckpt_iter))
m.vae_saver.restore(sess, save_name)
# prepare intepolate dir
intepolate_dir = join(sample_dir, 'interpolate_sample',
'%010d' % FLAGS.load_ckpt_iter)
tf.gfile.MakeDirs(intepolate_dir)
# things
interpolate_labels = [int(_) for _ in FLAGS.interpolate_labels.split(',')]
nb_images_between_labels = FLAGS.nb_images_between_labels
index_list_A = []
last_pos = [0] * 10
for label in interpolate_labels:
index_list_A.append(index_grouped_by_label_A[label][last_pos[label]])
last_pos[label] += 1
index_list_B = []
last_pos = [-1] * 10
for label in interpolate_labels:
index_list_B.append(index_grouped_by_label_B[label][last_pos[label]])
last_pos[label] -= 1
z_A = []
z_A.append(train_mu_A[index_list_A[0]])
for i_label in range(1, len(interpolate_labels)):
last_z_A = z_A[-1]
this_z_A = train_mu_A[index_list_A[i_label]]
for j in range(1, nb_images_between_labels + 1):
z_A.append(last_z_A +
(this_z_A - last_z_A) * (float(j) / nb_images_between_labels))
z_B = []
z_B.append(train_mu_B[index_list_B[0]])
for i_label in range(1, len(interpolate_labels)):
last_z_B = z_B[-1]
this_z_B = train_mu_B[index_list_B[i_label]]
for j in range(1, nb_images_between_labels + 1):
z_B.append(last_z_B +
(this_z_B - last_z_B) * (float(j) / nb_images_between_labels))
z_B_tr = []
for this_z_A in z_A:
this_z_B_tr = sess.run(m.x_A_to_B_direct, {m.x_A: np.array([this_z_A])})
z_B_tr.append(this_z_B_tr[0])
# Generate data domain instances and save.
z_A = np.array(z_A)
x_A = one_side_helper_A.m_helper.decode(z_A)
x_A = common.post_proc(x_A, one_side_helper_A.m_helper.config)
batched_x_A = common.batch_image(
x_A,
max_images=len(x_A),
rows=len(x_A),
cols=1,
)
common.save_image(batched_x_A, join(intepolate_dir, 'x_A.png'))
z_B = np.array(z_B)
x_B = one_side_helper_B.m_helper.decode(z_B)
x_B = common.post_proc(x_B, one_side_helper_B.m_helper.config)
batched_x_B = common.batch_image(
x_B,
max_images=len(x_B),
rows=len(x_B),
cols=1,
)
common.save_image(batched_x_B, join(intepolate_dir, 'x_B.png'))
z_B_tr = np.array(z_B_tr)
x_B_tr = one_side_helper_B.m_helper.decode(z_B_tr)
x_B_tr = common.post_proc(x_B_tr, one_side_helper_B.m_helper.config)
batched_x_B_tr = common.batch_image(
x_B_tr,
max_images=len(x_B_tr),
rows=len(x_B_tr),
cols=1,
)
common.save_image(batched_x_B_tr, join(intepolate_dir, 'x_B_tr.png'))
def main(unused_argv):
del unused_argv
# Load Config
config_name = FLAGS.config
config_module = importlib.import_module('configs.%s' % config_name)
config = config_module.config
model_uid = common.get_model_uid(config_name, FLAGS.exp_uid)
n_latent = config['n_latent']
# Load dataset
dataset = common.load_dataset(config)
basepath = dataset.basepath
save_path = dataset.save_path
train_data = dataset.train_data
# Make the directory
save_dir = os.path.join(save_path, model_uid)
best_dir = os.path.join(save_dir, 'best')
tf.gfile.MakeDirs(save_dir)
tf.gfile.MakeDirs(best_dir)
tf.logging.info('Save Dir: %s', save_dir)
# Set random seed
np.random.seed(FLAGS.random_seed)
tf.set_random_seed(FLAGS.random_seed)
# Load Model
tf.reset_default_graph()
sess = tf.Session()
with tf.device(tf.train.replica_device_setter(ps_tasks=0)):
m = model_dataspace.Model(config, name=model_uid)
_ = m() # noqa
# Initialize
sess.run(tf.global_variables_initializer())
# Load
m.vae_saver.restore(
sess, os.path.join(best_dir, 'vae_best_%s.ckpt' % model_uid))
# Sample from prior
sample_count = 64
image_path = os.path.join(basepath, 'sample', model_uid)
tf.gfile.MakeDirs(image_path)
# from prior
z_p = np.random.randn(sample_count, m.n_latent)
x_p = sess.run(m.x_mean, {m.z: z_p})
x_p = common.post_proc(x_p, config)
common.save_image(common.batch_image(x_p),
os.path.join(image_path, 'sample_prior.png'))
# Sample from priro, as Grid
boundary = 2.0
number_grid = 50
blob = common.make_grid(boundary=boundary,
number_grid=number_grid,
dim_latent=n_latent)
z_grid, dim_grid = blob.z_grid, blob.dim_grid
x_grid = sess.run(m.x_mean, {m.z: z_grid})
x_grid = common.post_proc(x_grid, config)
batch_image_grid = common.make_batch_image_grid(dim_grid, number_grid)
common.save_image(batch_image_grid(x_grid),
os.path.join(image_path, 'sample_grid.png'))
# Reconstruction
sample_count = 64
x_real = train_data[:sample_count]
mu, sigma = sess.run([m.mu, m.sigma], {m.x: x_real})
x_rec = sess.run(m.x_mean, {m.mu: mu, m.sigma: sigma})
x_rec = common.post_proc(x_rec, config)
x_real = common.post_proc(x_real, config)
common.save_image(common.batch_image(x_real),
os.path.join(image_path, 'image_real.png'))
common.save_image(common.batch_image(x_rec),
os.path.join(image_path, 'image_rec.png'))
def main(unused_argv):
del unused_argv
# Load Config
config_name = FLAGS.config
config_module = importlib.import_module('configs.%s' % config_name)
config = config_module.config
model_uid = common.get_model_uid(config_name, FLAGS.exp_uid)
n_latent = config['n_latent']
# Load dataset
dataset = common.load_dataset(config)
basepath = dataset.basepath
save_path = dataset.save_path
train_data = dataset.train_data
# Make the directory
save_dir = os.path.join(save_path, model_uid)
best_dir = os.path.join(save_dir, 'best')
tf.gfile.MakeDirs(save_dir)
tf.gfile.MakeDirs(best_dir)
tf.logging.info('Save Dir: %s', save_dir)
# Set random seed
np.random.seed(FLAGS.random_seed)
tf.set_random_seed(FLAGS.random_seed)
# Load Model
tf.reset_default_graph()
sess = tf.Session()
with tf.device(tf.train.replica_device_setter(ps_tasks=0)):
m = model_dataspace.Model(config, name=model_uid)
_ = m() # noqa
# Initialize
sess.run(tf.global_variables_initializer())
# Load
m.vae_saver.restore(sess,
os.path.join(best_dir, 'vae_best_%s.ckpt' % model_uid))
# Sample from prior
sample_count = 64
image_path = os.path.join(basepath, 'sample', model_uid)
tf.gfile.MakeDirs(image_path)
# from prior
z_p = np.random.randn(sample_count, m.n_latent)
x_p = sess.run(m.x_mean, {m.z: z_p})
x_p = common.post_proc(x_p, config)
common.save_image(
common.batch_image(x_p), os.path.join(image_path, 'sample_prior.png'))
# Sample from priro, as Grid
boundary = 2.0
number_grid = 50
blob = common.make_grid(
boundary=boundary, number_grid=number_grid, dim_latent=n_latent)
z_grid, dim_grid = blob.z_grid, blob.dim_grid
x_grid = sess.run(m.x_mean, {m.z: z_grid})
x_grid = common.post_proc(x_grid, config)
batch_image_grid = common.make_batch_image_grid(dim_grid, number_grid)
common.save_image(
batch_image_grid(x_grid), os.path.join(image_path, 'sample_grid.png'))
# Reconstruction
sample_count = 64
x_real = train_data[:sample_count]
mu, sigma = sess.run([m.mu, m.sigma], {m.x: x_real})
x_rec = sess.run(m.x_mean, {m.mu: mu, m.sigma: sigma})
x_rec = common.post_proc(x_rec, config)
x_real = common.post_proc(x_real, config)
common.save_image(
common.batch_image(x_real), os.path.join(image_path, 'image_real.png'))
common.save_image(
common.batch_image(x_rec), os.path.join(image_path, 'image_rec.png'))
