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})