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'))