flags.DEFINE_string("save_path", None, "Model output directory")
flags.DEFINE_string("config_file", None, "Model config file")
FLAGS = flags.FLAGS
if __name__ == "__main__":
_reset_rand_seed()
if not tf.gfile.Exists('./save'):
tf.gfile.MkDir('./save')
# Config stuff
config = get_config(FLAGS)
data_input = DataInput(config)
_reset_rand_seed()
train_batches = data_input.train_epoch_size
train_generator = data_input.batch_generator(True)
val_batches = data_input.val_epoch_size
val_generator = data_input.batch_generator(False)
# Model building
if config.model_type == 'motiongan':
model_wrap = get_model(config)
if FLAGS.verbose:
print('Discriminator model:')
print(model_wrap.disc_model.summary())
print('Generator model:')
print(model_wrap.gen_model.summary())
print('GAN model:')
print(model_wrap.gan_model.summary())
def _reset_rand_seed():
seed = 42
np.random.seed(seed)
if __name__ == "__main__":
# Config stuff
config = get_config(FLAGS)
# config.only_val = True
config.normalize_data = False
# config.pick_num = 0
data_input = DataInput(config)
_reset_rand_seed()
n_batches = 4
n_splits = 32
print('Plotting %d batches in %d splits for the %s dataset' %
(n_batches, n_splits, config.data_set))
for b in range(n_batches):
labs_batch, poses_batch = data_input.batch_generator(False).next()
n_seqs = (config.batch_size // n_splits)
for i in trange(n_splits):
plot_seq_gif(
poses_batch[i * n_seqs:(i + 1) * n_seqs, :, :, :3],
labs_batch[i * n_seqs:(i + 1) * n_seqs, ...],
config.data_set,
# save_path='save/vis_%s_%d_%d.gif' % (config.data_set, b, i),
figwidth=1920,
figheight=1080)