pc_gen = model.fit(data, noise, iter_no, config.dis_n_iter)
if iter_no % 1000 == 0:
sio.savemat('%srender_%d.mat' % (config.render_dir, iter_no),
{'X_hat': pc_gen})
# save image of point cloud
if iter_no % config.renders_every_iter == 0:
pc_gen = np.reshape(pc_gen[0, :], [2048, 3])
im_array = point_cloud_three_views(pc_gen)
img = Image.fromarray(np.uint8(im_array * 255.0))
img.save('%srender_%d.jpg' % (config.render_dir, iter_no))
if iter_no % config.save_every_iter == 0:
model.save_model(config.save_dir + 'model.ckpt')
if iter_no % 10000 == 0:
os.mkdir(config.save_dir + str(iter_no))
model.save_model(config.save_dir + str(iter_no) + '/model.ckpt')
if iter_no % 10:
with open(config.log_dir + 'start_iter', "w") as text_file:
text_file.write("%d" % iter_no)
# testing
for test_no in range(config.N_test):
noise = np.random.normal(size=[config.batch_size, config.z_size],
scale=0.2)
img = model.generate(noise)
from PIL import Image
import scipy.io as sio
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--class_name', default='', help='Shapenet class')
parser.add_argument('--render_dir', default='', help='Renders directory')
parser.add_argument('--save_dir', default='', help='Trained model directory')
param = parser.parse_args()
# import config
config = Config()
config.render_dir = param.render_dir
config.save_dir = param.save_dir
#class_name = raw_input('Give me the class name (e.g. "chair"): ').lower()
class_name = param.class_name
model = GAN(config)
model.do_variables_init()
model.restore_model(config.save_dir + 'model.ckpt')
# testing
for test_no in range(config.N_test):
noise = np.random.normal(size=[config.batch_size, config.z_size],
scale=0.2)
pc_gen = model.generate(noise)
sio.savemat('%srender.mat' % (config.render_dir, ), {'X_hat': pc_gen})
