def generate_vae_dataset( N=10000, test_p=0.9, use_cached=False, imsize=84, show=False, dataset_path=None, env_class=SawyerReachTorqueEnv, env_kwargs=None, init_camera=sawyer_torque_reacher_camera, ): filename = "/tmp/sawyer_torque_data" + str(N) + ".npy" info = {} if dataset_path is not None: filename = local_path_from_s3_or_local_path(dataset_path) dataset = np.load(filename) elif use_cached and osp.isfile(filename): dataset = np.load(filename) print("loaded data from saved file", filename) else: now = time.time() if env_kwargs == None: env_kwargs = dict() env = env_class(**env_kwargs) env = ImageEnv( env, imsize, transpose=True, init_camera=init_camera, normalize=True, ) info['env'] = env policy = RandomPolicy(env.action_space) es = OUStrategy(action_space=env.action_space, theta=0) exploration_policy = PolicyWrappedWithExplorationStrategy( exploration_strategy=es, policy=policy, ) dataset = np.zeros((N, imsize * imsize * 3), dtype=np.uint8) for i in range(N): if i % 50 == 0: print('Reset') env.reset_model() exploration_policy.reset() for _ in range(1): action = exploration_policy.get_action()[0] * 1 / 10 env.wrapped_env.step(action) img = env._get_flat_img() dataset[i, :] = unormalize_image(img) if show: cv2.imshow('img', img.reshape(3, 84, 84).transpose()) cv2.waitKey(1) print(i) print("done making training data", time.time() - now) np.save(filename, dataset) n = int(N * test_p) train_dataset = dataset[:n, :] test_dataset = dataset[n:, :] return train_dataset, test_dataset, info
def generate_vae_dataset( N=10000, test_p=0.9, use_cached=True, imsize=84, show=False, dataset_path=None, policy_path=None, action_space_sampling=False, env_class=SawyerDoorEnv, env_kwargs=None, init_camera=sawyer_door_env_camera_v2, ): if policy_path is not None: filename = "/tmp/sawyer_door_pull_open_oracle+random_policy_data_closer_zoom_action_limited" + str(N) + ".npy" elif action_space_sampling: filename = "/tmp/sawyer_door_pull_open_zoomed_in_action_space_sampling" + str(N) + ".npy" else: filename = "/tmp/sawyer_door_pull_open" + str(N) + ".npy" info = {} if dataset_path is not None: filename = local_path_from_s3_or_local_path(dataset_path) dataset = np.load(filename) elif use_cached and osp.isfile(filename): dataset = np.load(filename) print("loaded data from saved file", filename) else: now = time.time() env = env_class(**env_kwargs) env = ImageEnv( env, imsize, transpose=True, init_camera=init_camera, normalize=True, ) info['env'] = env policy = RandomPolicy(env.action_space) es = OUStrategy(action_space=env.action_space, theta=0) exploration_policy = PolicyWrappedWithExplorationStrategy( exploration_strategy=es, policy=policy, ) env.wrapped_env.reset() dataset = np.zeros((N, imsize * imsize * 3), dtype=np.uint8) for i in range(N): if i % 20==0: env.reset_model() exploration_policy.reset() for _ in range(10): action = exploration_policy.get_action()[0] env.wrapped_env.step( action ) # env.set_to_goal_angle(env.get_goal()['state_desired_goal']) img = env._get_flat_img() dataset[i, :] = unormalize_image(img) if show: cv2.imshow('img', img.reshape(3, 84, 84).transpose()) cv2.waitKey(1) print(i) print("done making training data", filename, time.time() - now) np.save(filename, dataset) n = int(N * test_p) train_dataset = dataset[:n, :] test_dataset = dataset[n:, :] return train_dataset, test_dataset, info