def __init__(self, args): # initialize environment self.args = args if args.vae_dist_help: load_vaes(args) self.env = make_env(args) self.args.timesteps = self.env.env.env.spec.max_episode_steps self.env_test = make_env(args) self.info = [] self.test_rollouts = 12 # get current policy from path (restore tf session + graph) self.play_dir = args.play_path self.play_epoch = args.play_epoch self.meta_path = "{}saved_policy-{}.meta".format(self.play_dir, self.play_epoch) self.sess = tf.Session() self.saver = tf.train.import_meta_graph(self.meta_path) self.saver.restore(self.sess, tf.train.latest_checkpoint(self.play_dir)) graph = tf.get_default_graph() self.raw_obs_ph = graph.get_tensor_by_name("raw_obs_ph:0") self.pi = graph.get_tensor_by_name("main/policy/net/pi/Tanh:0")
def flat_entries(bboxes_list, ppair): return np.concatenate([bboxes_list.ravel(), ppair.ravel()]) if __name__ == "__main__": args = get_args() # create data folder if it does not exist, corresponding folders, and files where to store data this_file_dir = os.path.dirname(os.path.abspath(__file__)) + '/' base_data_dir = this_file_dir + 'data/' env_data_dir = base_data_dir + args.env + '/' make_dir(env_data_dir, clear=False) if args.vae_dist_help: load_vaes(args) load_field_parameters(args) env = make_temp_env(args) field_names = ['ppair', 'bbox', 'distance'] csv_file_path = env_data_dir + 'distances.csv' csv_file_path_val = env_data_dir + 'distances_val.csv' csv_file_path_test = env_data_dir + 'distances_test.csv' for csv_path in [csv_file_path, csv_file_path_val, csv_file_path_test]: if os.path.exists(csv_path): os.remove(csv_path) with open(csv_path, 'w') as csv_file: writer = csv.DictWriter(csv_file, fieldnames=field_names) writer.writeheader() #create about 20 000 samples for each step
z_pres, z_depth, z_scale, z_pos = z_pres.detach().cpu().numpy(), z_depth.detach().cpu().numpy(), \ z_scale.detach().cpu().numpy(), z_pos.detach().cpu().numpy() return z_pres, z_depth, z_scale, z_pos if __name__ == "__main__": args = get_args() # create data folder if it does not exist, corresponding folders, and files where to store data this_file_dir = os.path.dirname(os.path.abspath(__file__)) + '/' base_data_dir = this_file_dir + 'data/' env_data_dir = base_data_dir + args.env + '/' make_dir(env_data_dir, clear=False) assert args.vae_dist_help load_vaes(args, doing_inference=True) load_field_parameters(args) env = gym.make(args.env) pres = [] scale = [] pos = [] for rs in range(10): env.reset() image = take_image_objects(None, args.img_size, direct_env=env.env) #im_current = Image.fromarray(image.astype(np.uint8)) #im_current.save('env_image_for_vae.png') z_pres, z_depth, z_scale, z_pos = extract_info(np.array([image]), args) pres.append(z_pres[0]) scale.append(z_scale[0])