def get_dataset_paths(dataset_name): if dataset_name == "push_box_hardware": episodes_root = os.path.join(get_data_ssd_root(), "dataset/push_box_hardware") episodes_config = load_yaml( os.path.join( get_project_root(), 'experiments/exp_22_push_box_hardware/push_box_hardware_episodes_config.yaml' )) transporter_model_chkpt = None dense_descriptor_model_chkpt = "/home/manuelli/data/key_dynam/dev/experiments/22/dataset_push_box_string_pull/trained_models/perception/dense_descriptors/data_aug_2020-07-02-02-39-27-400442/net_best_model.pth" return { 'dataset_name': dataset_name, 'dataset_root': episodes_root, 'episodes_config': episodes_config, 'main_camera_name': 'd415_01', 'dense_descriptor_camera_list': ['d415_01', 'd415_02'], 'transporter_model_chkpt': transporter_model_chkpt, 'dense_descriptor_model_chkpt': dense_descriptor_model_chkpt, } elif dataset_name == "push_box_string_pull": episodes_root = os.path.join(get_data_ssd_root(), "dataset/push_box_string_pull") episodes_config = load_yaml( os.path.join( get_project_root(), 'experiments/exp_22_push_box_hardware/push_box_string_pull_episodes_config.yaml' )) transporter_model_chkpt = None dense_descriptor_model_chkpt = "/home/manuelli/data/key_dynam/dev/experiments/22/dataset_push_box_string_pull/trained_models/perception/dense_descriptors/data_aug_2020-07-02-02-39-27-400442/net_best_model.pth" return { 'dataset_name': dataset_name, 'dataset_root': episodes_root, 'episodes_config': episodes_config, 'main_camera_name': 'd415_01', 'dense_descriptor_camera_list': ['d415_01', 'd415_02'], 'transporter_model_chkpt': transporter_model_chkpt, 'dense_descriptor_model_chkpt': dense_descriptor_model_chkpt, } else: raise ValueError("unknown dataset:", dataset_name)
def test_pusher_slider_dataset(): # dataset, config = create_pusher_slider_dataset() project_root = get_project_root() config_file = os.path.join(project_root, "experiments/01/config.yaml") config = load_yaml(config_file) # new dataset loading approach episodes = load_episodes_from_config(config) action_function = ActionFunctionFactory.function_from_config(config) observation_function = ObservationFunctionFactory.function_from_config( config) dataset = MultiEpisodeDataset(config, action_function=action_function, observation_function=observation_function, episodes=episodes, phase="train") data = dataset[0] # test the getitem print("type(data)", type(data)) print("list(data)", list(data)) print(type(data["observations"])) print("observations.shape", data["observations"].shape) print("actions.shape", data["actions"].shape) print("observations", data["observations"]) print("actions", data["actions"]) stats = dataset.compute_dataset_statistics() print("stats", stats)
def load_config(): config = load_yaml( os.path.join( get_project_root(), "experiments/exp_22_push_box_hardware/integral_heatmap_3d.yaml" )) return config
def load_autoencoder_model(): train_dir = "/home/manuelli/data/key_dynam/dev/experiments/drake_pusher_slider_v2/dataset_2020-04-20-14-58-21-418302_T_aug_random_velocity_1000/trained_models/dynamics/autoencoder/2020-06-08-19-08-45-269917_z_dim_16" chkpt_file = "net_dy_epoch_294_iter_100_state_dict.pth" ckpt_file = os.path.join(train_dir, chkpt_file) config = load_yaml(os.path.join(train_dir, 'config.yaml')) state_dict = torch.load(ckpt_file) # build dynamics model model_dy = build_dynamics_model(config) model_dy.load_state_dict(state_dict['dynamics']) model_dy = model_dy.eval() model_dy = model_dy.cuda() # build autoencoder file model_ae = ConvolutionalAutoencoder.from_global_config(config) model_ae.load_state_dict(state_dict['autoencoder']) model_ae = model_ae.eval() model_ae = model_ae.cuda() # visual observation function visual_observation_func = VisualObservationFunctionFactory.autoencoder_latent_state( config, model_ae=model_ae) return { 'model_ae': model_ae, 'model_dy': model_dy, 'visual_observation_function': visual_observation_func }
def load_model_from_checkpoint(model_chkpt_file): """ Assumes the config is stored 2 levels up :param model_ckpt_file: :type model_ckpt_file: :return: :rtype: """ train_dir = os.path.dirname(os.path.dirname(model_chkpt_file)) model_name = os.path.split(train_dir)[-1] config = load_yaml(os.path.join(train_dir, 'config.yaml')) model_kp = Transporter(config, use_gpu=True) model_kp.load_state_dict(torch.load(model_chkpt_file)) model_kp = model_kp.cuda() model_kp = model_kp.eval() return { 'model': model_kp, 'model_file': model_chkpt_file, 'model_name': None, 'train_dir': train_dir, 'config': config, }
def sample_random_mug(): sdf_dir = os.path.join(get_data_root(), "stable/sim_assets/anzu_mugs") # sdf_file = random.choice(SDFHelper.get_sdf_list(sdf_dir)) mug_list = load_yaml( os.path.join(get_project_root(), 'experiments/exp_20_mugs/mugs.yaml')) sdf_file = random.choice(mug_list['corelle_mug-small']) sdf_file = os.path.join(sdf_dir, sdf_file) return sdf_file
def top_down_dataset_root(): dataset_name = "2020-04-20-14-58-21-418302_T_aug_random_velocity_1000" # dataset_root = os.path.join(get_data_root(), "dev/experiments/09/data", dataset_name) dataset_root = os.path.join(get_data_ssd_root(), 'dataset', dataset_name) config = load_yaml(os.path.join(dataset_root, 'config.yaml')) return {'dataset_name': dataset_name, 'dataset_root': dataset_root, 'config': config, 'main_camera_name': 'camera_1_top_down'}
def load_default_config(): """ Loads the experiments/05/config.yaml :return: :rtype: """ config_file = os.path.join(get_project_root(), 'experiments/05/config.yaml') config = load_yaml(config_file) return config
def single_corelle_mug_600(): dataset_name = "single_corelle_mug_600" dataset_root = os.path.join(get_data_ssd_root(), 'dataset', dataset_name) config = load_yaml(os.path.join(dataset_root, 'config.yaml')) return { 'dataset_name': dataset_name, 'dataset_root': dataset_root, 'config': config, 'main_camera_name': 'camera_1_top_down', }
def mugs_random_colors_1000(): dataset_name = "mugs_random_colors_1000" dataset_root = os.path.join(get_data_ssd_root(), 'dataset', dataset_name) config = load_yaml(os.path.join(dataset_root, 'config.yaml')) return { 'dataset_name': dataset_name, 'dataset_root': dataset_root, 'config': config, 'main_camera_name': 'camera_1_top_down', }
def f(q_tmp): config = load_yaml( os.path.join(get_project_root(), 'experiments/exp_20_mugs/config.yaml')) config['dataset']['num_episodes'] = num_episodes_per_thread out = collect_episodes(config, output_dir=OUTPUT_DIR, visualize=False, debug=False, run_from_thread=True) q_tmp.put(out)
def angled_cam_dataset_root(): dataset_name = "2020-04-23-20-45-12-697915_T_aug_random_velocity_1000_angled_cam" # prepare folders # dataset_root = os.path.join(get_data_root(), 'dev/experiments/10/data', dataset_name) dataset_root = os.path.join(get_data_ssd_root(), 'dataset', dataset_name) config = load_yaml(os.path.join(dataset_root, 'config.yaml')) return {'dataset_name': dataset_name, 'dataset_root': dataset_root, 'config': config, 'main_camera_name': 'camera_angled', }
def get_dataset_paths(dataset_name): if dataset_name == "2020-04-20-14-58-21-418302_T_aug_random_velocity_1000": return top_down_dataset_root() elif dataset_name == "2020-04-23-20-45-12-697915_T_aug_random_velocity_1000_angled_cam": return angled_cam_dataset_root() elif dataset_name == "box_push_1000_top_down": dataset_root = os.path.join(get_data_ssd_root(), 'dataset', "box_push_1000") config = load_yaml(os.path.join(dataset_root, 'config.yaml')) transporter_model_chkpt = "/home/manuelli/data/key_dynam/dev/experiments/drake_pusher_slider_v2/dataset_box_push_1000_top_down/trained_models/perception/transporter/transporter_standard_2020-06-14-22-29-31-256422/train_nKp6_invStd10.0/net_best.pth" dense_descriptor_model_chkpt = "/home/manuelli/data/key_dynam/dev/experiments/drake_pusher_slider_v2/dataset_box_push_1000_top_down/trained_models/perception/dense_descriptors/data_aug_2020-06-14-21-47-52-389769/net_best_model.pth" return {'dataset_name': dataset_name, 'dataset_root': dataset_root, 'config': config, 'main_camera_name': 'camera_1_top_down', 'dense_descriptor_camera_list': ['camera_1_top_down', 'camera_2_top_down_rotated'], 'transporter_model_chkpt': transporter_model_chkpt, 'dense_descriptor_model_chkpt': dense_descriptor_model_chkpt, } elif dataset_name == "box_push_1000_angled": dataset_root = os.path.join(get_data_ssd_root(), 'dataset', "box_push_1000") config = load_yaml(os.path.join(dataset_root, 'config.yaml')) transporter_model_chkpt = "/home/manuelli/data/key_dynam/dev/experiments/drake_pusher_slider_v2/dataset_box_push_1000_angled/trained_models/perception/transporter/transporter_standard_2020-06-15-18-35-52-478769/train_nKp6_invStd10.0/net_best.pth" dense_descriptor_model_chkpt = "/home/manuelli/data/key_dynam/dev/experiments/drake_pusher_slider_v2/dataset_box_push_1000_angled/trained_models/perception/dense_descriptors/data_aug_2020-06-15-15-39-24-127276/net_best_model.pth" return {'dataset_name': dataset_name, 'dataset_root': dataset_root, 'config': config, 'main_camera_name': 'camera_angled', 'dense_descriptor_camera_list': ['camera_angled', 'camera_angled_rotated'], 'transporter_model_chkpt': transporter_model_chkpt, 'dense_descriptor_model_chkpt': dense_descriptor_model_chkpt, } else: raise ValueError("unknown dataset:", dataset_name)
def box_on_side_dataset_root(): dataset_name = "dps_box_on_side_600" # dataset_root = os.path.join(get_data_root(), "dev/experiments/18/data", dataset_name) dataset_root = os.path.join(get_data_ssd_root(), 'dataset', dataset_name) config = load_yaml(os.path.join(dataset_root, 'config.yaml')) dense_descriptor_model_chkpt = "/home/manuelli/data/key_dynam/dev/experiments/drake_pusher_slider_box_on_side/dataset_dps_box_on_side_600/trained_models/perception/dense_descriptor/3D_loss_camera_angled_2020-05-13-23-39-35-818188/net_best_dy_model.pth" return {'dataset_name': dataset_name, 'dataset_root': dataset_root, 'config': config, 'main_camera_name': 'camera_angled', 'dense_descriptor_model_chkpt': dense_descriptor_model_chkpt, }
def correlle_mug_small_many_colors_600(): dataset_name = "correlle_mug-small_many_colors_600" dataset_root = os.path.join(get_data_ssd_root(), 'dataset', dataset_name) config = load_yaml(os.path.join(dataset_root, 'config.yaml')) dense_descriptor_model_chkpt = "/home/manuelli/data/key_dynam/dev/experiments/20/dataset_correlle_mug-small_many_colors_600/trained_models/perception/dense_descriptors/data_aug_2020-06-03-16-41-29-740641/net_best_model.pth" return { 'dataset_name': dataset_name, 'dataset_root': dataset_root, 'config': config, 'main_camera_name': 'camera_1_top_down', "dense_descriptor_model_chkpt": dense_descriptor_model_chkpt, }
def test_pusher_slider_keypoint_dataset(): project_root = get_project_root() config_file = os.path.join(project_root, "experiments/02/config.yaml") config = load_yaml(config_file) config["n_history"] = 1 config["n_roll"] = 0 # new dataset loading approach episodes = load_episodes_from_config(config) action_function = ActionFunctionFactory.function_from_config(config) observation_function = ObservationFunctionFactory.function_from_config( config) dataset = MultiEpisodeDataset(config, action_function=action_function, observation_function=observation_function, episodes=episodes, phase="train") # dataset, config = create_pusher_slider_keypoint_dataset(config=config) episode_names = dataset.get_episode_names() episode_names.sort() episode_name = episode_names[0] episode = dataset.episode_dict[episode_name] obs_raw = episode.get_observation(0) obs_raw['slider']['angle'] = 0 dataset.observation_function(obs_raw) print("20 degrees\n\n\n\n") obs_raw['slider']['angle'] = np.deg2rad(90) dataset.observation_function(obs_raw) quit() data = dataset[0] # test the getitem print("type(data)", type(data)) print("data.keys()", data.keys()) print(type(data["observations"])) print("observations.shape", data["observations"].shape) print("actions.shape", data["actions"].shape) print("observations", data["observations"]) print("actions", data["actions"])
def load_model(): # dataset_name # model_file sae_train_dir = "/home/manuelli/data/key_dynam/dev/experiments/drake_pusher_slider_v2/dataset_2020-04-20-14-58-21-418302_T_aug_random_velocity_1000/trained_models/perception/spatial_autoencoder" # model_name = "2020-06-05-20-57-10-394927" ckp_file = 'net_best.pth' model_name = "2020-06-06-01-57-53-187767" # lr 1e-3 model_name = "2020-06-06-17-31-05-356659" # with masked loss ckp_file = "net_kp_epoch_38_iter_0.pth" dataset_name = "2020-04-20-14-58-21-418302_T_aug_random_velocity_1000" train_dir = os.path.join(sae_train_dir, model_name) dataset_paths = exp_dps_utils.get_dataset_paths(dataset_name) config = load_yaml(os.path.join(train_dir, 'config.yaml')) ckp_file = os.path.join(train_dir, 'checkpoints', ckp_file) camera_name = config['perception']['camera_name'] model = SpatialAutoencoder.from_global_config(config) model.load_state_dict(torch.load(ckp_file)) dataset_root = dataset_paths['dataset_root'] dataset_name = dataset_paths['dataset_name'] multi_episode_dict = DCDrakeSimEpisodeReader.load_dataset(dataset_root=dataset_root) image_preprocess_func = AutoencoderImagePreprocessFunctionFactory.spatial_autoencoder(config) dataset = AutoencoderImageDataset(config=config, episodes=multi_episode_dict, phase="train", camera_names=[camera_name], image_preprocess_func=image_preprocess_func, ) return {'dataset_name': dataset_name, 'dataset': dataset, 'model': model}
def load_transporter_model(model_file=None): train_dir = os.path.dirname(os.path.dirname(model_file)) print("train_dir", train_dir) config = load_yaml(os.path.join(train_dir, 'config.yaml')) model_kp = Transporter(config, use_gpu=True) model_kp.load_state_dict(torch.load(model_file)) model_kp = model_kp.cuda() model_kp = model_kp.eval() return { 'model': model_kp, 'model_file': model_file, 'train_dir': train_dir, 'config': config, }
def load_dynamics_model_from_folder(model_folder, state_dict_file=None, strict=True): """ Builds model and loads parameters using the 'load_state_dict' function """ config = load_yaml(os.path.join(model_folder, 'config.yaml')) model = build_dynamics_model(config) if state_dict_file is None: state_dict_file = os.path.join(model_folder, 'net_best_dy_state_dict.pth') model.load_state_dict(torch.load(state_dict_file), strict=strict) model = model.eval() model = model.cuda() _, model_name = os.path.split(model_folder) return {'model_dy': model, 'model_name': model_name, 'config': config}
def main(): start_time = time.time() config = load_yaml(os.path.join(get_project_root(), 'experiments/drake_pusher_slider/env_config.yaml')) config['dataset']['num_episodes'] = 1000 # half for train, half for valid set_seed(500) # just randomly chosen num_episodes = config['dataset']['num_episodes'] DATASET_NAME = "box_push_%d" %(num_episodes) OUTPUT_DIR = os.path.join(get_data_ssd_root(), 'dataset', DATASET_NAME) if not os.path.exists(OUTPUT_DIR): os.makedirs(OUTPUT_DIR) collect_episodes( config, output_dir=OUTPUT_DIR, visualize=False, debug=False) elapsed = time.time() - start_time print("Generating and saving dataset to disk took %d seconds" % (int(elapsed)))
def load_model(model_folder, strict=True): model_dy_dict = model_builder.load_dynamics_model_from_folder( model_folder, strict=strict) _, model_name = os.path.split(model_folder) config = model_dy_dict['config'] # correct way precomputed_data_root = config['dataset']['precomputed_data_root'] metadata = load_yaml(os.path.join(precomputed_data_root, 'metadata.yaml')) model_kp_file = metadata['model_file'] print("model_kp_file", model_kp_file) model_kp_dict = load_transporter_model(model_file=model_kp_file) return { "model_dy": model_dy_dict, 'model_kp': model_kp_dict, 'model_name': model_name }
def main(): start_time = time.time() config = load_yaml( os.path.join(get_project_root(), 'experiments/exp_20_mugs/config.yaml')) config['dataset']['num_episodes'] = 10 set_seed(500) # just randomly chosen DATASET_NAME = "mugs_%d" % (config['dataset']['num_episodes']) OUTPUT_DIR = os.path.join(get_data_root(), 'sandbox', DATASET_NAME) print("OUTPUT_DIR:", OUTPUT_DIR) collect_episodes(config, output_dir=OUTPUT_DIR, visualize=False, debug=False) elapsed = time.time() - start_time print("Generating and saving dataset to disk took %d seconds" % (int(elapsed)))
def create_pusher_slider_keypoint_dataset(config=None): # load some previously generated data project_root = get_project_root() if config is None: config_file = os.path.join(project_root, "experiments/02/config.yaml") config = load_yaml(config_file) action_function = ActionFunctionFactory.pusher_velocity obs_function = ObservationFunctionFactory.pusher_pose_slider_keypoints( config) DATA_PATH = os.path.join( project_root, "test_data/pusher_slider_10_episodes/2019-10-22-21-30-02-536750.p") raw_data = load_pickle(DATA_PATH) episodes = PyMunkEpisodeReader.load_pymunk_episodes_from_raw_data(raw_data) # create MultiEpisodeDataset dataset = MultiEpisodeDataset(config, action_function=action_function, observation_function=obs_function, episodes=episodes) episode = dataset.get_random_episode() data_0 = episode.get_observation(0) data_1 = episode.get_observation(1) print("time 0", data_0["sim_time"]) print("time 1", data_1["sim_time"]) # episode_name = episodes.keys()[0] # episode = episodes[episode_name] # data = episode.data # print("episode.data.keys()", episode.data.keys()) # print("test ", type(data["trajectory"][0].keys())) # print("test ", data["trajectory"][0].keys()) return dataset, config
def run_interactive_circle_slider(): """ Launch interactive environment where you can move the pusher around with the arrow keys :return: :rtype: """ config_file = os.path.join(get_project_root(), 'experiments/03/config.yaml') config = load_yaml(config_file) env = PusherSlider(config=config) env.reset() while env._running: action = env.process_events() env.step(action) obs, reward, done, info = env.step(action) env.render(mode='human') if True: print("\n\n\n") print("slider position", obs['slider']['position']) print("pusher position", obs['pusher']['position'])
def main(): start_time = time.time() config = load_yaml( os.path.join(get_project_root(), 'experiments/exp_18_box_on_side/config.yaml')) # config['dataset']['num_episodes'] = 500 # half for train, half for valid config['dataset']['num_episodes'] = 600 # half for train, half for valid set_seed(500) # just randomly chosen DATASET_NAME = "dps_box_on_side_%d" % (config['dataset']['num_episodes']) OUTPUT_DIR = os.path.join(get_data_root(), "dev/experiments/18/data", DATASET_NAME) print("OUTPUT_DIR:", OUTPUT_DIR) collect_episodes(config, output_dir=OUTPUT_DIR, visualize=False, debug=False) elapsed = time.time() - start_time print("Generating and saving dataset to disk took %d seconds" % (int(elapsed)))
def load_autoencoder_model(train_dir): chkpt_file = "net_best_state_dict.pth" ckpt_file = os.path.join(train_dir, chkpt_file) config = load_yaml(os.path.join(train_dir, 'config.yaml')) state_dict = torch.load(ckpt_file) # build dynamics model model_dy = build_dynamics_model(config) model_dy.load_state_dict(state_dict['dynamics']) model_dy = model_dy.eval() model_dy = model_dy.cuda() # build autoencoder file model_ae = ConvolutionalAutoencoder.from_global_config(config) model_ae.load_state_dict(state_dict['autoencoder']) model_ae = model_ae.eval() model_ae = model_ae.cuda() return { 'model_ae': model_ae, 'model_dy': model_dy, 'config': config, }
def main(): # load dynamics model model_dict = load_model_state_dict() model = model_dict['model_dy'] model_dd = model_dict['model_dd'] config = model.config env_config = load_yaml(os.path.join(get_project_root(), 'experiments/exp_20_mugs/config.yaml')) env_config['env']['observation']['depth_int16'] = True n_history = config['train']['n_history'] initial_cond = generate_initial_condition(env_config, push_length=PUSH_LENGTH) env_config = initial_cond['config'] # enable the right observations camera_name = model_dict['metadata']['camera_name'] spatial_descriptor_data = model_dict['spatial_descriptor_data'] ref_descriptors = spatial_descriptor_data['spatial_descriptors'] K = ref_descriptors.shape[0] ref_descriptors = torch.Tensor(ref_descriptors).cuda() # put them on the GPU print("ref_descriptors\n", ref_descriptors) print("ref_descriptors.shape", ref_descriptors.shape) # create the environment # create the environment env = DrakeMugsEnv(env_config) env.reset() T_world_camera = env.camera_pose(camera_name) camera_K_matrix = env.camera_K_matrix(camera_name) # create another environment for doing rollouts env2 = DrakeMugsEnv(env_config, visualize=False) env2.reset() action_function = ActionFunctionFactory.function_from_config(config) observation_function = ObservationFunctionFactory.drake_pusher_position_3D(config) visual_observation_function = \ VisualObservationFunctionFactory.descriptor_keypoints_3D(config=config, camera_name=camera_name, model_dd=model_dd, ref_descriptors=ref_descriptors, K_matrix=camera_K_matrix, T_world_camera=T_world_camera, ) episode = OnlineEpisodeReader() mpc_input_builder = DynamicsModelInputBuilder(observation_function=observation_function, visual_observation_function=visual_observation_function, action_function=action_function, episode=episode) vis = meshcat_utils.make_default_visualizer_object() vis.delete() reset_environment(env, initial_cond['q_pusher'], initial_cond['q_slider']) obs_init = env.get_observation() #### ROLLOUT USING LEARNED MODEL + GROUND TRUTH ACTIONS ############ reset_environment(env, initial_cond['q_pusher'], initial_cond['q_slider']) # add just some large number of these episode.clear() for i in range(n_history): action_zero = np.zeros(2) obs_tmp = env.get_observation() episode.add_observation_action(obs_tmp, action_zero) def goal_func(obs_tmp): state_tmp = mpc_input_builder.get_state_input_single_timestep({'observation': obs_tmp})['state'] return model.compute_z_state(state_tmp.unsqueeze(0))['z_object'].flatten() # idx = episode.get_latest_idx() obs_raw = episode.get_observation(idx) z_object_goal = goal_func(obs_raw) z_keypoints_init_W = keypoints_3D_from_dynamics_model_output(z_object_goal, K) z_keypoints_init_W = torch_utils.cast_to_numpy(z_keypoints_init_W) z_keypoints_obj = keypoints_world_frame_to_object_frame(z_keypoints_init_W, T_W_obj=slider_pose_from_observation(obs_init)) color = [1, 0, 0] meshcat_utils.visualize_points(vis=vis, name="keypoints_W", pts=z_keypoints_init_W, color=color, size=0.02, ) # input("press Enter to continue") # rollout single action sequence using the simulator action_sequence_np = torch_utils.cast_to_numpy(initial_cond['action_sequence']) N = action_sequence_np.shape[0] obs_rollout_gt = env_utils.rollout_action_sequence(env, action_sequence_np)[ 'observations'] # using the vision model to get "goal" keypoints z_object_goal = goal_func(obs_rollout_gt[-1]) z_object_goal_np = torch_utils.cast_to_numpy(z_object_goal) z_keypoints_goal = keypoints_3D_from_dynamics_model_output(z_object_goal, K) z_keypoints_goal = torch_utils.cast_to_numpy(z_keypoints_goal) # visualize goal keypoints color = [0, 1, 0] meshcat_utils.visualize_points(vis=vis, name="goal_keypoints", pts=z_keypoints_goal, color=color, size=0.02, ) # input("press Enter to continue") #### ROLLOUT USING LEARNED MODEL + GROUND TRUTH ACTIONS ############ reset_environment(env, initial_cond['q_pusher'], initial_cond['q_slider']) # add just some large number of these episode.clear() for i in range(n_history): action_zero = np.zeros(2) obs_tmp = env.get_observation() episode.add_observation_action(obs_tmp, action_zero) # [n_history, state_dim] idx = episode.get_latest_idx() dyna_net_input = mpc_input_builder.get_dynamics_model_input(idx, n_history=n_history) state_init = dyna_net_input['states'].cuda() # [n_history, state_dim] action_init = dyna_net_input['actions'] # [n_history, action_dim] print("state_init.shape", state_init.shape) print("action_init.shape", action_init.shape) action_seq_gt_torch = torch_utils.cast_to_torch(initial_cond['action_sequence']) action_input = torch.cat((action_init[:(n_history-1)], action_seq_gt_torch), dim=0).cuda() print("action_input.shape", action_input.shape) # rollout using the ground truth actions and learned model # need to add the batch dim to do that z_init = model.compute_z_state(state_init)['z'] rollout_pred = rollout_model(state_init=z_init.unsqueeze(0), action_seq=action_input.unsqueeze(0), dynamics_net=model, compute_debug_data=True) state_pred_rollout = rollout_pred['state_pred'] print("state_pred_rollout.shape", state_pred_rollout.shape) for i in range(N): # vis GT for now name = "GT_3D/%d" % (i) T_W_obj = slider_pose_from_observation(obs_rollout_gt[i]) # print("T_W_obj", T_W_obj) # green color = np.array([0, 1, 0]) * get_color_intensity(i, N) meshcat_utils.visualize_points(vis=vis, name=name, pts=z_keypoints_obj, color=color, size=0.01, T=T_W_obj) # red color = np.array([0, 0, 1]) * get_color_intensity(i, N) state_pred = state_pred_rollout[:, i, :] pts_pred = keypoints_3D_from_dynamics_model_output(state_pred, K).squeeze() pts_pred = pts_pred.detach().cpu().numpy() name = "pred_3D/%d" % (i) meshcat_utils.visualize_points(vis=vis, name=name, pts=pts_pred, color=color, size=0.01, ) # input("finished visualizing GT rollout\npress Enter to continue") index_dict = get_object_and_robot_state_indices(config) object_indices = index_dict['object_indices'] # reset the environment and use the MPC controller to stabilize this # now setup the MPC to try to stabilize this . . . . reset_environment(env, initial_cond['q_pusher'], initial_cond['q_slider']) episode.clear() # add just some large number of these for i in range(n_history): action_zero = np.zeros(2) obs_tmp = env.get_observation() episode.add_observation_action(obs_tmp, action_zero) # input("press Enter to continue") # make a planner config planner_config = copy.copy(config) config_tmp = load_yaml(os.path.join(get_project_root(), 'experiments/drake_pusher_slider/eval_config.yaml')) planner_config['mpc'] = config_tmp['mpc'] planner = None if PLANNER_TYPE == "random_shooting": planner = RandomShootingPlanner(planner_config) elif PLANNER_TYPE == "mppi": planner = PlannerMPPI(planner_config) else: raise ValueError("unknown planner type: %s" % (PLANNER_TYPE)) mpc_out = None action_seq_mpc = None state_pred_mpc = None counter = -1 while True: counter += 1 print("\n\n-----Running MPC Optimization: Counter (%d)-------" % (counter)) obs_cur = env.get_observation() episode.add_observation_only(obs_cur) if counter == 0 or REPLAN: print("replanning") ####### Run the MPC ########## # [1, state_dim] n_look_ahead = N - counter if USE_FIXED_MPC_HORIZON: n_look_ahead = MPC_HORIZON if n_look_ahead == 0: break # start_time = time.time() # idx of current observation idx = episode.get_latest_idx() mpc_start_time = time.time() mpc_input_data = mpc_input_builder.get_dynamics_model_input(idx, n_history=n_history) state_cur = mpc_input_data['states'] action_his = mpc_input_data['actions'] if mpc_out is not None: action_seq_rollout_init = mpc_out['action_seq'][1:] else: action_seq_rollout_init = None # run MPPI z_cur = None with torch.no_grad(): z_cur = model.compute_z_state(state_cur.unsqueeze(0).cuda())['z'].squeeze(0) mpc_out = planner.trajectory_optimization(state_cur=z_cur, action_his=action_his, obs_goal=z_object_goal_np, model_dy=model, action_seq_rollout_init=action_seq_rollout_init, n_look_ahead=n_look_ahead, eval_indices=object_indices, rollout_best_action_sequence=True, verbose=True, ) print("MPC step took %.4f seconds" %(time.time() - mpc_start_time)) action_seq_mpc = mpc_out['action_seq'].cpu().numpy() # Rollout with ground truth simulator dynamics action_seq_mpc = torch_utils.cast_to_numpy(mpc_out['action_seq']) env2.set_simulator_state_from_observation_dict(env2.get_mutable_context(), obs_cur) obs_mpc_gt = env_utils.rollout_action_sequence(env2, action_seq_mpc)['observations'] state_pred_mpc = torch_utils.cast_to_numpy(mpc_out['state_pred']) vis['mpc_3D'].delete() vis['mpc_GT_3D'].delete() L = len(obs_mpc_gt) print("L", L) if L == 0: break for i in range(L): # red color = np.array([1, 0, 0]) * get_color_intensity(i, L) state_pred = state_pred_mpc[i, :] state_pred = np.expand_dims(state_pred, 0) # may need to expand dims here pts_pred = keypoints_3D_from_dynamics_model_output(state_pred, K).squeeze() name = "mpc_3D/%d" % (i) meshcat_utils.visualize_points(vis=vis, name=name, pts=pts_pred, color=color, size=0.01, ) # ground truth rollout of the MPC action_seq name = "mpc_GT_3D/%d" % (i) T_W_obj = slider_pose_from_observation(obs_mpc_gt[i]) # green color = np.array([1, 1, 0]) * get_color_intensity(i, L) meshcat_utils.visualize_points(vis=vis, name=name, pts=z_keypoints_obj, color=color, size=0.01, T=T_W_obj) action_cur = action_seq_mpc[0] print("action_cur", action_cur) # print("action_GT", initial_cond['action']) input("press Enter to continue") # add observation actions to the episode obs_cur = env.get_observation() episode.replace_observation_action(obs_cur, action_cur) # step the simulator env.step(action_cur) # visualize current keypoint positions obs_cur = env.get_observation() T_W_obj = slider_pose_from_observation(obs_cur) # yellow color = np.array([1, 1, 0]) meshcat_utils.visualize_points(vis=vis, name="keypoint_cur", pts=z_keypoints_obj, color=color, size=0.02, T=T_W_obj) action_seq_mpc = action_seq_mpc[1:] state_pred_mpc = state_pred_mpc[1:] obs_final = env.get_observation() pose_error = compute_pose_error(obs_rollout_gt[-1], obs_final) print("position_error: %.3f" %(pose_error['position_error'])) print("angle error degrees: %.3f" %(pose_error['angle_error_degrees']))
def load_dataset( dataset_root, # str: folder containing dataset load_image_data=True, descriptor_images_root=None, # str: (optional) folder containing hdf5 files with descriptors descriptor_keypoints_root=None, max_num_episodes=None, # int, max num episodes to load precomputed_data_root=None, ): """ :param dataset_root: folder should contain - config.yaml - metadata.yaml - <episode_name.p> - <episode_name.h5> :type dataset_root: :return: :rtype: """ if load_image_data: from key_dynam.dense_correspondence.dc_drake_sim_episode_reader import DCDrakeSimEpisodeReader metadata = load_yaml(DrakeSimEpisodeReader.metadata_file(dataset_root)) multi_episode_dict = dict() episode_names = list(metadata['episodes'].keys()) episode_names.sort() # sort the keys num_episodes = len(episode_names) # optionally don't read all episodes if (max_num_episodes is not None) and (max_num_episodes > 0): # compute the number of episodes to read, in sorted order num_episodes = int(min(len(episode_names), max_num_episodes)) for idx in range(num_episodes): episode_name = episode_names[idx] val = metadata['episodes'][episode_name] # load non image data non_image_data_file = os.path.join(dataset_root, val['non_image_data_file']) assert os.path.isfile( non_image_data_file), "File doesn't exist: %s" % ( non_image_data_file) non_image_data = load_pickle(non_image_data_file) dc_episode_reader = None if load_image_data: # load image data image_data_file = os.path.join(dataset_root, val['image_data_file']) assert os.path.isfile( image_data_file), "File doesn't exist: %s" % ( image_data_file) descriptor_image_data_file = None if descriptor_images_root is not None: descriptor_image_data_file = os.path.join( descriptor_images_root, val['image_data_file']) assert os.path.isfile( descriptor_image_data_file ), "File doesn't exist: %s" % (descriptor_image_data_file) descriptor_keypoints_data = None descriptor_keypoints_hdf5_file = None if descriptor_keypoints_root is not None: # replace .h5 filename with .p for pickle file descriptor_keypoints_data_file = val[ 'image_data_file'].split(".")[0] + ".p" descriptor_keypoints_data_file = os.path.join( descriptor_keypoints_root, descriptor_keypoints_data_file) descriptor_keypoints_hdf5_file = os.path.join( descriptor_keypoints_root, val['image_data_file']) if os.path.isfile(descriptor_keypoints_data_file): descriptor_keypoints_data = load_pickle( descriptor_keypoints_data_file) else: assert os.path.isfile( descriptor_keypoints_hdf5_file ), "File doesn't exist: %s" % ( descriptor_keypoints_hdf5_file) #### precomputed_data = None precomputed_data_file = None if precomputed_data_root is not None: # replace .h5 filename with .p for pickle file precomputed_data_file = val['image_data_file'].split( ".")[0] + ".p" precomputed_data_file = os.path.join( precomputed_data_root, precomputed_data_file) if os.path.isfile(precomputed_data_file): precomputed_data = load_pickle(precomputed_data_file) else: raise ValueError("file doesn't exist: %s" % (precomputed_data_file)) dc_episode_reader = DCDrakeSimEpisodeReader( non_image_data, image_data_file, descriptor_image_data_file=descriptor_image_data_file, descriptor_keypoints_data=descriptor_keypoints_data, descriptor_keypoints_data_file= descriptor_keypoints_hdf5_file, precomputed_data=precomputed_data, precomputed_data_file=precomputed_data_file) episode_reader = DrakeSimEpisodeReader( non_image_data=non_image_data, episode_name=episode_name, dc_episode_reader=dc_episode_reader) multi_episode_dict[episode_name] = episode_reader return multi_episode_dict
def load_simple_config(): config_file = os.path.join(get_project_root(), 'config/simple_config.yaml') config = load_yaml(config_file) return config
def run_gym_env(): """ Runs the gym env :return: :rtype: """ DEBUG_PRINTS = True USE_PYGAME = True try: # setup pygame thing if USE_PYGAME: pygame.init() screen = pygame.display.set_mode( (640, 480)) # needed for grabbing focus clock = pygame.time.Clock() default_action = np.zeros(2) velocity = 0.2 if USE_PYGAME: action = process_pygame_events() else: action = default_action config = load_yaml( os.path.join(get_project_root(), 'experiments/exp_20_mugs/config.yaml')) env = DrakeMugsEnv(config) env.reset() context = env.get_mutable_context() pos = np.array([0, 0, 0.1]) # quat = transforms3d.euler.euler2quat(np.deg2rad(90), 0, 0) quat = np.array([1, 0, 0, 0]) q = np.concatenate((quat, pos)) env.set_object_position(context=context, q=q) # set the box pose # context = env.get_mutable_context() # pos = np.array([1.56907481e-04, 1.11390697e-06, 5.11972761e-02]) # quat = np.array([ 7.13518047e-01, -6.69765583e-07, -7.00636851e-01, -6.82079212e-07]) # q_slider = np.concatenate((quat, pos)) # env.set_slider_position(context, q=q_slider) env.simulator.set_target_realtime_rate(1.0) # move box araound # context = env.get_mutable_context() # q_slider = [-0.05, 0, 0.03] num_model_instances = env.diagram_wrapper.mbp.num_model_instances() print("num_model_instances", num_model_instances) print("num_positions", env.diagram_wrapper.mbp.num_positions()) label_db = env.diagram_wrapper.get_label_db() print("label db:", label_db.all()) mask_db = env.diagram_wrapper.get_labels_to_mask() print("mask db:", mask_db.all()) # context = env.get_mutable_context() # # # set the position of pusher # q_pusher = [0.2,0.2] # mbp = env.diagram_wrapper.mbp # mbp_context = env.diagram.GetMutableSubsystemContext(mbp, context) # mbp.SetPositions(mbp_context, env.diagram_wrapper.models['pusher'], q_pusher) camera_names = list( config['env']['rgbd_sensors']['sensor_list'].keys()) camera_names.sort() image_vis = ImageVisualizer(len(camera_names), 1) print("running sim") while True: if USE_PYGAME: action = velocity * process_pygame_events() else: action = default_action # print("action:", action) obs, reward, done, info = env.step(action) # print("obs\n", obs) # visualize RGB images in matplotlib for idx, camera_name in enumerate(camera_names): rgb_image = obs['images'][camera_name]['rgb'] image_vis.draw_image(idx, 0, rgb_image) image_vis.visualize_interactive() # # print unique depth values # depth_32F = obs['images']['camera_0']['depth_32F'] # print("unique depth_32F vals", np.unique(depth_32F)) # # print unique depth values # depth_16U = obs['images']['camera_0']['depth_16U'] # print("unique depth_16U vals", np.unique(depth_16U)) # build simulator except KeyboardInterrupt: pygame.quit() plt.close()