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 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 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 evaluate_mpc( model_dy, # dynamics model env, # the environment episode, # OnlineEpisodeReader mpc_input_builder, # DynamicsModelInputBuilder planner, # RandomShooting planner eval_indices=None, goal_func=None, # function that gets goal from observation config=None, wait_for_user_input=False, save_dir=None, model_name="", experiment_name="", generate_initial_condition_func=None, # (optional) function to generate initial condition, takes episode length N as parameter ): if not os.path.exists(save_dir): os.makedirs(save_dir) # must specify initial condition distribution assert generate_initial_condition_func is not None save_yaml(config, os.path.join(save_dir, 'config.yaml')) writer = SummaryWriter(log_dir=save_dir) pandas_data_list = [] for episode_length in config['eval']['episode_length']: counter = 0 seed = 0 while counter < config['eval']['num_episodes']: start_time = time.time() seed += 1 set_seed(seed) # make it repeatable # initial_cond = generate_initial_condition(config, N=episode_length) initial_cond = generate_initial_condition_func(N=episode_length) env.set_initial_condition_from_dict(initial_cond) action_sequence_np = torch_utils.cast_to_numpy( initial_cond['action_sequence']) episode_data = mpc_single_episode( model_dy=model_dy, env=env, action_sequence=action_sequence_np, action_zero=np.zeros(2), episode=episode, mpc_input_builder=mpc_input_builder, planner=planner, eval_indices=eval_indices, goal_func=goal_func, config=config, wait_for_user_input=wait_for_user_input, ) # continue if invalid if not episode_data['valid']: print("invalid episode, skipping") continue pose_error = compute_pose_error( obs=episode_data['obs_mpc_final'], obs_goal=episode_data['obs_goal'], ) object_delta = compute_pose_error( obs=episode_data['obs_init'], obs_goal=episode_data['obs_goal']) print("object_delta\n", object_delta) if wait_for_user_input: print("pose_error\n", pose_error) pandas_data = { 'episode_length': episode_length, 'seed': counter, 'model_name': model_name, 'experiment_name': experiment_name, 'object_pos_delta': object_delta['position_error'], 'object_angle_delta': object_delta['angle_error'], 'object_angle_delta_degrees': object_delta['angle_error_degrees'], } pandas_data.update(pose_error) pandas_data_list.append(pandas_data) # log to tensorboard for key, val in pose_error.items(): plot_name = "%s/episode_len_%d" % (key, episode_length) writer.add_scalar(plot_name, val, counter) writer.flush() print("episode [%d/%d], episode_length %d, duration %.2f" % (counter, config['eval']['num_episodes'], episode_length, time.time() - start_time)) counter += 1 df_tmp = pd.DataFrame(pandas_data_list) keys = ["angle_error_degrees", "position_error"] for key in keys: for i in range(10): mean = df_tmp[key][df_tmp.episode_length == episode_length].mean() median = df_tmp[key][df_tmp.episode_length == episode_length].median() plot_name_mean = "mean/%s/episode_len_%d" % (key, episode_length) writer.add_scalar(plot_name_mean, mean, i) plot_name_median = "median/%s/episode_len_%d" % ( key, episode_length) writer.add_scalar(plot_name_median, median, i) # save some data df = pd.DataFrame(pandas_data_list) df.to_csv(os.path.join(save_dir, "data.csv"))
def eval_dynamics( config, eval_dir, # str: directory to save output multi_episode_dict=None, n_rollout_list=None, model_dy=None, # should already be in eval mode phase_list=None, # typically it's num_epochs=10, ): assert n_rollout_list is not None assert model_dy is not None assert multi_episode_dict is not None if phase_list is None: phase_list = ["valid"] # set random seed for reproduction set_seed(config['train']['random_seed']) tensorboard_dir = os.path.join(eval_dir, "tensorboard") if not os.path.exists(tensorboard_dir): os.makedirs(tensorboard_dir) writer = SummaryWriter(log_dir=tensorboard_dir) # save the config save_yaml(config, os.path.join(eval_dir, "config.yaml")) action_function = ActionFunctionFactory.function_from_config(config) observation_function = ObservationFunctionFactory.function_from_config( config) use_gpu = torch.cuda.is_available() best_valid_loss = np.inf global_iteration = 0 counters = {'train': 0, 'valid': 0} epoch_counter_external = 0 stats = dict() for n_rollout in n_rollout_list: stats[n_rollout] = dict() config_tmp = copy.copy(config) config_tmp['train']['n_rollout'] = n_rollout for phase in phase_list: stats[n_rollout][phase] = dict() print("Loading data for %s" % phase) dataset = MultiEpisodeDataset( config_tmp, action_function=action_function, observation_function=observation_function, episodes=multi_episode_dict, phase=phase) dataloader = DataLoader(dataset, batch_size=config['train']['batch_size'], shuffle=True, num_workers=config['train']['num_workers'], drop_last=True) loss_tensor_container = {"l2_avg": [], "l2_final_step": []} step_duration_meter = AverageMeter() global_iteration = 0 for epoch in range(num_epochs): for i, data in enumerate(dataloader): loss_container = dict() # store the losses for this step # types of losses ["l2_avg", "l2_final_step"] step_start_time = time.time() global_iteration += 1 counters[phase] += 1 with torch.no_grad(): n_his = config['train']['n_history'] n_roll = n_rollout n_samples = n_his + n_roll if DEBUG: print("global iteration: %d" % (global_iteration)) print("n_samples", n_samples) # [B, n_samples, obs_dim] observations = data['observations'] # [B, n_samples, action_dim] actions = data['actions'] B = actions.shape[0] if use_gpu: observations = observations.cuda() actions = actions.cuda() # states, actions = data assert actions.shape[1] == n_samples loss_mse = 0. # we don't have any visual observations, so states are observations states = observations # state_cur: B x n_his x state_dim # state_cur = states[:, :n_his] # [B, n_his, state_dim] state_init = states[:, :n_his] # We want to rollout n_roll steps # actions = [B, n_his + n_roll, -1] # so we want action_seq.shape = [B, n_roll, -1] action_start_idx = 0 action_end_idx = n_his + n_roll - 1 action_seq = actions[:, action_start_idx: action_end_idx, :] if DEBUG: print("states.shape", states.shape) print("state_init.shape", state_init.shape) print("actions.shape", actions.shape) print("action_seq.shape", action_seq.shape) # try using models_dy.rollout_model instead of doing this manually rollout_data = rollout_model(state_init=state_init, action_seq=action_seq, dynamics_net=model_dy, compute_debug_data=False) # [B, n_roll, state_dim] state_rollout_pred = rollout_data['state_pred'] # [B, n_roll, state_dim] state_rollout_gt = states[:, n_his:] if DEBUG: print("state_rollout_gt.shape", state_rollout_gt.shape) print("state_rollout_pred.shape", state_rollout_pred.shape) # the loss function is between # [B, n_roll, state_dim] state_pred_err = state_rollout_pred - state_rollout_gt # [B] l2_avg_tensor = torch.mean(torch.norm(state_pred_err, dim=-1), dim=1).detach().cpu() l2_avg = l2_avg_tensor.mean() # [B] l2_final_step_tensor = torch.norm( state_pred_err[:, -1], dim=-1).detach().cpu() l2_final_step = l2_final_step_tensor.mean() loss_tensor_container["l2_avg"].append(l2_avg_tensor) loss_container["l2_avg"] = l2_avg loss_tensor_container["l2_final_step"].append( l2_final_step_tensor) loss_container["l2_final_step"] = l2_final_step step_duration_meter.update(time.time() - step_start_time) if (i % config['train']['log_per_iter'] == 0) or (global_iteration % config['train']['log_per_iter'] == 0): # print some logging information log = "" log += ', step time %.6f' % (step_duration_meter.avg) # log data to tensorboard for loss_type, loss_obj in loss_container.items(): plot_name = "%s/n_roll_%s/%s" % (loss_type, n_roll, phase) writer.add_scalar(plot_name, loss_obj.item(), global_iteration) log += " %s: %.6f," % (plot_name, loss_obj.item()) print(log) writer.flush() # flush SummaryWriter events to disk stats[n_rollout][phase] = dict() for loss_type in loss_tensor_container: t = torch.cat(loss_tensor_container[loss_type]) mean = t.mean() median = t.median() std = t.std() stats[n_rollout][phase][loss_type] = { 'mean': mean, 'median': median, 'std': std } for stat_type, val in stats[n_rollout][phase][loss_type].items( ): plot_name = "stats/%s/n_roll_%d/%s/%s" % ( loss_type, n_roll, phase, stat_type) for idx_tmp in [0, 10, 100]: writer.add_scalar(plot_name, val, idx_tmp)
def main(): d = load_model_and_data() model_dy = d['model_dy'] dataset = d['dataset'] config = d['config'] multi_episode_dict = d['multi_episode_dict'] planner = d['planner'] planner_config = planner.config idx_dict = get_object_and_robot_state_indices(config) object_indices = idx_dict['object_indices'] robot_indices = idx_dict['robot_indices'] n_his = config['train']['n_history'] # save_dir = os.path.join(get_project_root(), 'sandbox/mpc/', get_current_YYYY_MM_DD_hh_mm_ss_ms()) save_dir = os.path.join(get_project_root(), 'sandbox/mpc/push_right_box_horizontal') print("save_dir", save_dir) if not os.path.exists(save_dir): os.makedirs(save_dir) # rotate # episode_names = dataset.get_episode_names() # print("len(episode_names)", len(episode_names)) # episode_name = episode_names[0] # start_idx = 1 # n_roll = 15 # # straight + rotate # episode_name = "2020-06-29-21-04-16" # print('episode_name', episode_name) # start_idx = 1 # n_roll = 15 # this is a nice straight push . . . # push with box in horizontal position episode_name = "2020-06-29-22-03-45" start_idx = 2 n_roll = 10 # # validation set episodes # episode_names = dataset.get_episode_names() # print("len(episode_names)", len(episode_names)) # episode_name = episode_names[1] # start_idx = 2 # n_roll = 15 camera_name = "d415_01" episode = multi_episode_dict[episode_name] print("episode_name", episode_name) vis = meshcat_utils.make_default_visualizer_object() vis.delete() idx_list = list(range(start_idx, start_idx + n_roll + 1)) idx_list_GT = idx_list goal_idx = idx_list[-1] print("idx_list", idx_list) # visualize ground truth rollout if True: for display_idx, episode_idx in enumerate(idx_list): visualize_episode_data_single_timestep( vis=vis, dataset=dataset, episode=episode, camera_name=camera_name, episode_idx=episode_idx, display_idx=episode_idx, ) data_goal = dataset._getitem(episode, goal_idx, rollout_length=1, n_history=1) states_goal = data_goal['observations_combined'][0] z_states_goal = model_dy.compute_z_state(states_goal)['z'] print("states_goal.shape", states_goal.shape) print("z_states_goal.shape", z_states_goal.shape) ##### VISUALIZE PREDICTED ROLLOUT ########## data = dataset._getitem(episode, start_idx, rollout_length=n_roll) states = data['observations_combined'].unsqueeze(0) z = model_dy.compute_z_state(states)['z'] actions = data['actions'].unsqueeze(0) idx_range_model_dy_input = data['idx_range'] print("data.keys()", data.keys()) print("data['idx_range']", data['idx_range']) # z_init z_init = z[:, :n_his] # actions_init action_start_idx = 0 action_end_idx = n_his + n_roll - 1 action_seq = actions[:, action_start_idx:action_end_idx] print("action_seq GT\n", action_seq) with torch.no_grad(): rollout_data = rollout_model(state_init=z_init.cuda(), action_seq=action_seq.cuda(), dynamics_net=model_dy, compute_debug_data=False) # [B, n_roll, state_dim] # state_rollout_pred = rollout_data['state_pred'] z_rollout_pred = rollout_data['state_pred'].squeeze() print("z_rollout_pred.shape", z_rollout_pred.shape) if True: for idx in range(len(z_rollout_pred)): display_idx = data['idx_range'][idx + n_his] visualize_model_prediction_single_timestep( vis, config, z_pred=z_rollout_pred[idx], display_idx=display_idx) print("z_rollout_pred.shape", z_rollout_pred.shape) # compute loss when rolled out using GT action sequence eval_indices = object_indices obs_goal = z_states_goal[object_indices].cuda() reward_data = planner_utils.evaluate_model_rollout( state_pred=rollout_data['state_pred'], obs_goal=obs_goal, eval_indices=eval_indices, terminal_cost_only=planner_config['mpc']['mppi']['terminal_cost_only'], p=planner_config['mpc']['mppi']['cost_norm']) print("reward_data using action_seq_GT\n", reward_data['reward']) ##### MPC ########## data = dataset._getitem(episode, start_idx, rollout_length=0, n_history=config['train']['n_history']) state_cur = data['observations_combined'].cuda() z_state_cur = model_dy.compute_z_state(state_cur)['z'] action_his = data['actions'][:(n_his - 1)].cuda() print("z_state_cur.shape", state_cur.shape) print("action_his.shape", action_his.shape) # don't seed with nominal actions just yet action_seq_rollout_init = None set_seed(SEED) mpc_out = planner.trajectory_optimization( state_cur=z_state_cur, action_his=action_his, obs_goal=obs_goal, model_dy=model_dy, action_seq_rollout_init=action_seq_rollout_init, n_look_ahead=n_roll, eval_indices=object_indices, rollout_best_action_sequence=True, verbose=True, add_grid_action_samples=True, ) print("\n\n------MPC output-------\n\n") print("action_seq:\n", mpc_out['action_seq']) mpc_state_pred = mpc_out['state_pred'] # current shape is [n_roll + 1, state_dim] but really should be # [n_roll, state_dim] . . . something is up print("mpc_state_pred.shape", mpc_state_pred.shape) print("mpc_out['action_seq'].shape", mpc_out['action_seq'].shape) print("n_roll", n_roll) # visualize for idx in range(n_roll): episode_idx = start_idx + idx + 1 visualize_model_prediction_single_timestep(vis, config, z_pred=mpc_state_pred[idx], display_idx=episode_idx, name_prefix="mpc", color=[255, 0, 0]) ######## MPC w/ dynamics model input builder ############# print("\n\n-----DynamicsModelInputBuilder-----") # dynamics model input builder online_episode = OnlineEpisodeReader(no_copy=True) ref_descriptors = d['spatial_descriptor_data']['spatial_descriptors'] ref_descriptors = torch_utils.cast_to_torch(ref_descriptors).cuda() K_matrix = episode.image_episode.camera_K_matrix(camera_name) T_world_camera = episode.image_episode.camera_pose(camera_name, 0) visual_observation_function = \ VisualObservationFunctionFactory.descriptor_keypoints_3D(config=config, camera_name=camera_name, model_dd=d['model_dd'], ref_descriptors=ref_descriptors, K_matrix=K_matrix, T_world_camera=T_world_camera, ) input_builder = DynamicsModelInputBuilder( observation_function=d['observation_function'], visual_observation_function=visual_observation_function, action_function=d['action_function'], episode=online_episode) compute_control_action_msg = dict() compute_control_action_msg['type'] = "COMPUTE_CONTROL_ACTION" for i in range(n_his): episode_idx = idx_range_model_dy_input[i] print("episode_idx", episode_idx) # add image information to data = add_images_to_episode_data(episode, episode_idx, camera_name) online_episode.add_data(copy.deepcopy(data)) compute_control_action_msg['data'] = data # hack for seeing how much the history matters .. . # online_episode.add_data(copy.deepcopy(data)) # save informatin for running zmq controller save_pickle(compute_control_action_msg, os.path.join(save_dir, 'compute_control_action_msg.p')) goal_idx = idx_list_GT[-1] goal_data = add_images_to_episode_data(episode, goal_idx, camera_name) goal_data['observations']['timestamp_system'] = time.time() plan_msg = { 'type': "PLAN", 'data': [goal_data], 'n_roll': n_roll, 'K_matrix': K_matrix, 'T_world_camera': T_world_camera, } save_pickle(plan_msg, os.path.join(save_dir, "plan_msg.p")) print("len(online_episode)", len(online_episode)) # use this to construct input # verify it's the same as what we got from using the dataset directly idx = online_episode.get_latest_idx() mpc_input_data = input_builder.get_dynamics_model_input(idx, n_history=n_his) # print("mpc_input_data\n", mpc_input_data) state_cur_ib = mpc_input_data['states'].cuda() action_his_ib = mpc_input_data['actions'].cuda() z_state_cur_ib = model_dy.compute_z_state(state_cur_ib)['z'] set_seed(SEED) mpc_out = planner.trajectory_optimization( state_cur=z_state_cur_ib, action_his=action_his_ib, obs_goal=obs_goal, model_dy=model_dy, action_seq_rollout_init=None, n_look_ahead=n_roll, eval_indices=object_indices, rollout_best_action_sequence=True, verbose=True, add_grid_action_samples=True, ) # visualize for idx in range(n_roll): episode_idx = start_idx + idx + 1 visualize_model_prediction_single_timestep( vis, config, z_pred=mpc_out['state_pred'][idx], display_idx=episode_idx, name_prefix="mpc_input_builder", color=[255, 255, 0])
def train_explore_and_learn( config, train_dir, # str: directory to save output data_dir, visualize=False): # set random seed for reproduction set_seed(config['train_explore_and_learn']['random_seed']) tensorboard_dir = os.path.join(train_dir, "tensorboard") if not os.path.exists(tensorboard_dir): os.makedirs(tensorboard_dir) writer = SummaryWriter(log_dir=tensorboard_dir) # save the config save_yaml(config, os.path.join(train_dir, "config.yaml")) print(config) num_exploration_rounds = config['train_explore_and_learn'][ 'num_exploration_rounds'] num_episodes_per_exploration_round = config['train_explore_and_learn'][ 'num_episodes_per_exploration_round'] num_timesteps = config['train_explore_and_learn']['num_timesteps'] # model_folder = os.path.join(train_dir, "../2020-04-05-23-00-30-887903") # model_file = os.path.join(model_folder, "net_best_dy_model.pth") # model_dy = torch.load(model_file) model_dy = None global_iteration = 0 ##### setup to store the dataset metadata = dict() metadata['episodes'] = dict() # data collector data_collector = DrakePusherSliderEpisodeCollector(config) ##### explore and learn for idx_exploration_round in range(num_exploration_rounds): print("Exploration round %d / %d" % (idx_exploration_round, num_exploration_rounds)) ### exploration if idx_exploration_round == 0: # initial exploration exploration_type = 'random' else: exploration_type = 'mppi' collect_episodes( config, metadata, data_collector, num_episodes_per_exploration_round, data_dir, visualize, exploration_type, model_dy=None if exploration_type == 'random' else model_dy) save_yaml(metadata, os.path.join(data_dir, 'metadata.yaml')) ### optimize the dynamics model model_dy, global_iteration = train_dynamics(config, train_dir, data_dir, model_dy, global_iteration, writer)
def train_dynamics( config, train_dir, # str: directory to save output ): # set random seed for reproduction set_seed(config['train']['random_seed']) st_epoch = config['train'][ 'resume_epoch'] if config['train']['resume_epoch'] > 0 else 0 tee = Tee(os.path.join(train_dir, 'train_st_epoch_%d.log' % st_epoch), 'w') tensorboard_dir = os.path.join(train_dir, "tensorboard") if not os.path.exists(tensorboard_dir): os.makedirs(tensorboard_dir) writer = SummaryWriter(log_dir=tensorboard_dir) # save the config save_yaml(config, os.path.join(train_dir, "config.yaml")) print(config) # load the data episodes = load_episodes_from_config(config) action_function = ActionFunctionFactory.function_from_config(config) observation_function = ObservationFunctionFactory.function_from_config( config) datasets = {} dataloaders = {} data_n_batches = {} for phase in ['train', 'valid']: print("Loading data for %s" % phase) datasets[phase] = MultiEpisodeDataset( config, action_function=action_function, observation_function=observation_function, episodes=episodes, phase=phase) dataloaders[phase] = DataLoader( datasets[phase], batch_size=config['train']['batch_size'], shuffle=True if phase == 'train' else False, num_workers=config['train']['num_workers']) data_n_batches[phase] = len(dataloaders[phase]) use_gpu = torch.cuda.is_available() # compute normalization parameters if not starting from pre-trained network . . . ''' define model for dynamics prediction ''' model_dy = None if config['train']['resume_epoch'] >= 0: # if resume from a pretrained checkpoint state_dict_path = os.path.join( train_dir, 'net_dy_epoch_%d_iter_%d_state_dict.pth' % (config['train']['resume_epoch'], config['train']['resume_iter'])) print("Loading saved ckp from %s" % state_dict_path) # why is this needed if we already do torch.load??? model_dy.load_state_dict(torch.load(state_dict_path)) # don't we also need to load optimizer state from pre-trained??? else: # not starting from pre-trained create the network and compute the # normalization parameters model_dy = DynaNetMLP(config) # compute normalization params stats = datasets["train"].compute_dataset_statistics() obs_mean = stats['observations']['mean'] obs_std = stats['observations']['std'] observations_normalizer = DataNormalizer(obs_mean, obs_std) action_mean = stats['actions']['mean'] action_std = stats['actions']['std'] actions_normalizer = DataNormalizer(action_mean, action_std) model_dy.action_normalizer = actions_normalizer model_dy.state_normalizer = observations_normalizer print("model_dy #params: %d" % count_trainable_parameters(model_dy)) # criterion criterionMSE = nn.MSELoss() # optimizer params = model_dy.parameters() optimizer = optim.Adam(params, lr=config['train']['lr'], betas=(config['train']['adam_beta1'], 0.999)) scheduler = ReduceLROnPlateau(optimizer, 'min', factor=0.9, patience=10, verbose=True) if use_gpu: model_dy = model_dy.cuda() best_valid_loss = np.inf global_iteration = 0 epoch_counter_external = 0 try: for epoch in range(st_epoch, config['train']['n_epoch']): phases = ['train', 'valid'] epoch_counter_external = epoch writer.add_scalar("Training Params/epoch", epoch, global_iteration) for phase in phases: model_dy.train(phase == 'train') meter_loss_rmse = AverageMeter() # bar = ProgressBar(max_value=data_n_batches[phase]) loader = dataloaders[phase] for i, data in enumerate(loader): global_iteration += 1 with torch.set_grad_enabled(phase == 'train'): n_his, n_roll = config['train']['n_history'], config[ 'train']['n_rollout'] n_samples = n_his + n_roll if config['env']['type'] in ['PusherSlider']: states = data['observations'] actions = data['actions'] if use_gpu: states = states.cuda() actions = actions.cuda() # states, actions = data assert states.size(1) == n_samples # normalize states and actions once for entire rollout states = model_dy.state_normalizer.normalize( states) actions = model_dy.action_normalizer.normalize( actions) B = states.size(0) loss_mse = 0. # state_cur: B x n_his x state_dim state_cur = states[:, :n_his] for j in range(n_roll): state_des = states[:, n_his + j] # action_cur: B x n_his x action_dim action_cur = actions[:, j:j + n_his] if actions is not None else None # state_pred: B x state_dim # state_cur: B x n_his x state_dim # state_pred: B x state_dim state_pred = model_dy(state_cur, action_cur) loss_mse_cur = criterionMSE( state_pred, state_des) loss_mse += loss_mse_cur / n_roll # update state_cur # state_pred.unsqueeze(1): B x 1 x state_dim state_cur = torch.cat([ state_cur[:, 1:], state_pred.unsqueeze(1) ], 1) meter_loss_rmse.update(np.sqrt(loss_mse.item()), B) if phase == 'train': optimizer.zero_grad() loss_mse.backward() optimizer.step() if i % config['train']['log_per_iter'] == 0: log = '%s [%d/%d][%d/%d] LR: %.6f' % ( phase, epoch, config['train']['n_epoch'], i, data_n_batches[phase], get_lr(optimizer)) log += ', rmse: %.6f (%.6f)' % (np.sqrt( loss_mse.item()), meter_loss_rmse.avg) print(log) # log data to tensorboard # only do it once we have reached 500 iterations if global_iteration > 500: writer.add_scalar("Params/learning rate", get_lr(optimizer), global_iteration) writer.add_scalar("Loss/train", loss_mse.item(), global_iteration) writer.add_scalar("RMSE average loss/train", meter_loss_rmse.avg, global_iteration) if phase == 'train' and i % config['train'][ 'ckp_per_iter'] == 0: save_model( model_dy, '%s/net_dy_epoch_%d_iter_%d' % (train_dir, epoch, i)) log = '%s [%d/%d] Loss: %.6f, Best valid: %.6f' % ( phase, epoch, config['train']['n_epoch'], meter_loss_rmse.avg, best_valid_loss) print(log) if phase == 'valid': scheduler.step(meter_loss_rmse.avg) writer.add_scalar("RMSE average loss/valid", meter_loss_rmse.avg, global_iteration) if meter_loss_rmse.avg < best_valid_loss: best_valid_loss = meter_loss_rmse.avg save_model(model_dy, '%s/net_best_dy' % (train_dir)) writer.flush() # flush SummaryWriter events to disk except KeyboardInterrupt: # save network if we have a keyboard interrupt save_model( model_dy, '%s/net_dy_epoch_%d_keyboard_interrupt' % (train_dir, epoch_counter_external)) writer.flush() # flush SummaryWriter events to disk
def main(dataset_name): # sample from specific image dataset_paths = None episode_name = None camera_name = None episode_idx = None if dataset_name == "dps_box_on_side_600": camera_name = "camera_angled" episode_name = "2020-05-13-21-55-01-487901_idx_33" episode_idx = 22 dataset_paths = exp_18_utils.get_dataset_paths(dataset_name) elif dataset_name == "correlle_mug-small_single_color_600": camera_name = "camera_1_top_down" episode_name = "2020-06-02-14-15-27-898104_idx_56" episode_idx = 18 dataset_paths = exp_20_utils.get_dataset_paths(dataset_name) elif dataset_name == "correlle_mug-small_many_colors_600": camera_name = "camera_1_top_down" episode_name = "2020-06-03-15-48-50-165064_idx_56" episode_idx = 20 dataset_paths = exp_20_utils.get_dataset_paths(dataset_name) else: raise ValueError("unknown dataset") dataset_root = dataset_paths['dataset_root'] dataset_name = dataset_paths['dataset_name'] ## Load Model model_name, model_file = get_DD_model_file(dataset_name) model_train_dir = os.path.dirname(model_file) print("model_train_dir", model_train_dir) print("model_file", model_file) model = torch.load(model_file) model = model.cuda() model = model.eval() multi_episode_dict = DCDrakeSimEpisodeReader.load_dataset( dataset_root, max_num_episodes=None) output_dir = os.path.join( model_train_dir, 'precomputed_vision_data/descriptor_keypoints/dataset_%s/' % (dataset_name)) # compute descriptor confidence scores if False: print("\n\n---------Computing Descriptor Confidence Scores-----------") metadata_file = os.path.join(output_dir, 'metadata.p') if os.path.isfile(metadata_file): answer = input( "metadata.p file already exists, do you want to overwrite it? y/n\n" ) if answer == "y": shutil.rmtree(output_dir) print("removing existing file and continuing") else: print("aborting") quit() set_seed(0) compute_descriptor_confidences( multi_episode_dict, model, output_dir, batch_size=10, num_workers=20, model_file=model_file, camera_name=camera_name, num_ref_descriptors=50, num_batches=10, episode_name_arg=episode_name, episode_idx=episode_idx, ) if False: confidence_score_data_file = os.path.join(output_dir, 'data.p') confidence_score_data = load_pickle(confidence_score_data_file) metadata_file = os.path.join(output_dir, 'metadata.p') metadata = load_pickle(metadata_file) print( "\n\n---------Selecting Spatially Separated Keypoints-----------") score_and_select_spatially_separated_keypoints( metadata, confidence_score_data=confidence_score_data, K=4, position_diff_threshold=15, output_dir=output_dir, ) # visualize descriptors if False: metadata_file = os.path.join(output_dir, 'metadata.p') metadata = load_pickle(metadata_file) episode_name = metadata['episode_name'] episode_idx = metadata['episode_idx'] camera_name = metadata['camera_name'] episode = multi_episode_dict[episode_name] data = episode.get_image_data(camera_name, episode_idx) rgb = data['rgb'] uv = metadata['indices'] print("uv.shape", uv.shape) color = [0, 255, 0] draw_reticles(rgb, uv[:, 0], uv[:, 1], label_color=color) save_file = os.path.join(output_dir, 'sampled_descriptors.png') plt.figure() plt.imshow(rgb) plt.savefig(save_file) plt.show() # visualize spatially separated descriptors if False: metadata_file = os.path.join(output_dir, 'metadata.p') metadata = load_pickle(metadata_file) spatial_descriptor_file = os.path.join(output_dir, 'spatial_descriptors.p') spatial_descriptors_data = load_pickle(spatial_descriptor_file) des_idx = spatial_descriptors_data['spatial_descriptors_idx'] episode_name = metadata['episode_name'] episode_idx = metadata['episode_idx'] camera_name = metadata['camera_name'] episode = multi_episode_dict[episode_name] data = episode.get_image_data(camera_name, episode_idx) rgb = data['rgb'] uv = metadata['indices'] print("uv.shape", uv.shape) color = [0, 255, 0] draw_reticles(rgb, uv[des_idx, 0], uv[des_idx, 1], label_color=color) save_file = os.path.join(output_dir, 'spatially_separated_descriptors.png') plt.figure() plt.imshow(rgb) plt.savefig(save_file) plt.show() if True: metadata_file = os.path.join(output_dir, 'metadata.p') metadata = load_pickle(metadata_file) # metadata_file = "/media/hdd/data/key_dynam/dev/experiments/09/precomputed_vision_data/dataset_2020-03-25-19-57-26-556093_constant_velocity_500/model_name_2020-04-07-14-31-35-804270_T_aug_dataset/2020-04-09-20-51-50-624799/metadata.p" # metadata = load_pickle(metadata_file) print("\n\n---------Precomputing Descriptor Keypoints-----------") descriptor_keypoints_output_dir = os.path.join(output_dir, "descriptor_keypoints") precompute_descriptor_keypoints(multi_episode_dict, model, descriptor_keypoints_output_dir, ref_descriptors_metadata=metadata, batch_size=8, num_workers=20, camera_names=[camera_name]) print("Data saved at: ", output_dir) print("Finished Normally")
def train_dynamics( config, train_dir, # str: directory to save output multi_episode_dict=None, visual_observation_function=None, metadata=None, spatial_descriptors_data=None, ): assert multi_episode_dict is not None # assert spatial_descriptors_idx is not None # set random seed for reproduction set_seed(config['train']['random_seed']) st_epoch = config['train'][ 'resume_epoch'] if config['train']['resume_epoch'] > 0 else 0 tee = Tee(os.path.join(train_dir, 'train_st_epoch_%d.log' % st_epoch), 'w') tensorboard_dir = os.path.join(train_dir, "tensorboard") if not os.path.exists(tensorboard_dir): os.makedirs(tensorboard_dir) writer = SummaryWriter(log_dir=tensorboard_dir) # save the config save_yaml(config, os.path.join(train_dir, "config.yaml")) if metadata is not None: save_pickle(metadata, os.path.join(train_dir, 'metadata.p')) if spatial_descriptors_data is not None: save_pickle(spatial_descriptors_data, os.path.join(train_dir, 'spatial_descriptors.p')) training_stats = dict() training_stats_file = os.path.join(train_dir, 'training_stats.yaml') action_function = ActionFunctionFactory.function_from_config(config) observation_function = ObservationFunctionFactory.function_from_config( config) datasets = {} dataloaders = {} data_n_batches = {} for phase in ['train', 'valid']: print("Loading data for %s" % phase) datasets[phase] = MultiEpisodeDataset( config, action_function=action_function, observation_function=observation_function, episodes=multi_episode_dict, phase=phase, visual_observation_function=visual_observation_function) print("len(datasets[phase])", len(datasets[phase])) dataloaders[phase] = DataLoader( datasets[phase], batch_size=config['train']['batch_size'], shuffle=True if phase == 'train' else False, num_workers=config['train']['num_workers'], drop_last=True) data_n_batches[phase] = len(dataloaders[phase]) use_gpu = torch.cuda.is_available() # compute normalization parameters if not starting from pre-trained network . . . if False: dataset = datasets["train"] data = dataset[0] print("data['observations_combined'].shape", data['observations_combined'].shape) print("data.keys()", data.keys()) print("data['observations_combined']", data['observations_combined'][0]) print("data['observations_combined'].shape", data['observations_combined'].shape) print("data['actions'].shape", data['actions'].shape) print("data['actions']\n", data['actions']) quit() ''' Build model for dynamics prediction ''' model_dy = build_dynamics_model(config) if config['dynamics_net'] == "mlp_weight_matrix": raise ValueError("can't use weight matrix with standard setup") # criterion criterionMSE = nn.MSELoss() l1Loss = nn.L1Loss() smoothL1 = nn.SmoothL1Loss() # optimizer params = model_dy.parameters() lr = float(config['train']['lr']) optimizer = optim.Adam(params, lr=lr, betas=(config['train']['adam_beta1'], 0.999)) # setup scheduler sc = config['train']['lr_scheduler'] scheduler = None if config['train']['lr_scheduler']['enabled']: if config['train']['lr_scheduler']['type'] == "ReduceLROnPlateau": scheduler = ReduceLROnPlateau(optimizer, mode='min', factor=sc['factor'], patience=sc['patience'], threshold_mode=sc['threshold_mode'], cooldown=sc['cooldown'], verbose=True) elif config['train']['lr_scheduler']['type'] == "StepLR": step_size = config['train']['lr_scheduler']['step_size'] gamma = config['train']['lr_scheduler']['gamma'] scheduler = StepLR(optimizer, step_size=step_size, gamma=gamma) else: raise ValueError("unknown scheduler type: %s" % (config['train']['lr_scheduler']['type'])) if use_gpu: print("using gpu") model_dy = model_dy.cuda() # print("model_dy.vision_net._ref_descriptors.device", model_dy.vision_net._ref_descriptors.device) # print("model_dy.vision_net #params: %d" %(count_trainable_parameters(model_dy.vision_net))) best_valid_loss = np.inf valid_loss_type = config['train']['valid_loss_type'] global_iteration = 0 counters = {'train': 0, 'valid': 0} epoch_counter_external = 0 loss = 0 try: for epoch in range(st_epoch, config['train']['n_epoch']): phases = ['train', 'valid'] epoch_counter_external = epoch writer.add_scalar("Training Params/epoch", epoch, global_iteration) for phase in phases: # only validate at a certain frequency if (phase == "valid") and ( (epoch % config['train']['valid_frequency']) != 0): continue model_dy.train(phase == 'train') average_meter_container = dict() step_duration_meter = AverageMeter() # bar = ProgressBar(max_value=data_n_batches[phase]) loader = dataloaders[phase] for i, data in enumerate(loader): loss_container = dict() # store the losses for this step step_start_time = time.time() global_iteration += 1 counters[phase] += 1 with torch.set_grad_enabled(phase == 'train'): n_his, n_roll = config['train']['n_history'], config[ 'train']['n_rollout'] n_samples = n_his + n_roll if DEBUG: print("global iteration: %d" % (global_iteration)) print("n_samples", n_samples) # [B, n_samples, obs_dim] states = data['observations_combined'] # [B, n_samples, action_dim] actions = data['actions'] B = actions.shape[0] if use_gpu: states = states.cuda() actions = actions.cuda() # state_cur: B x n_his x state_dim # state_cur = states[:, :n_his] # [B, n_his, state_dim] state_init = states[:, :n_his] # We want to rollout n_roll steps # actions = [B, n_his + n_roll, -1] # so we want action_seq.shape = [B, n_roll, -1] action_start_idx = 0 action_end_idx = n_his + n_roll - 1 action_seq = actions[:, action_start_idx: action_end_idx, :] if DEBUG: print("states.shape", states.shape) print("state_init.shape", state_init.shape) print("actions.shape", actions.shape) print("action_seq.shape", action_seq.shape) # try using models_dy.rollout_model instead of doing this manually rollout_data = rollout_model(state_init=state_init, action_seq=action_seq, dynamics_net=model_dy, compute_debug_data=False) # [B, n_roll, state_dim] state_rollout_pred = rollout_data['state_pred'] # [B, n_roll, state_dim] state_rollout_gt = states[:, n_his:] if DEBUG: print("state_rollout_gt.shape", state_rollout_gt.shape) print("state_rollout_pred.shape", state_rollout_pred.shape) # the loss function is between # [B, n_roll, state_dim] state_pred_err = state_rollout_pred - state_rollout_gt # everything is in 3D space now so no need to do any scaling # all the losses would be in meters . . . . loss_mse = criterionMSE(state_rollout_pred, state_rollout_gt) loss_l1 = l1Loss(state_rollout_pred, state_rollout_gt) loss_l2 = torch.norm(state_pred_err, dim=-1).mean() loss_smoothl1 = smoothL1(state_rollout_pred, state_rollout_gt) loss_smoothl1_final_step = smoothL1( state_rollout_pred[:, -1], state_rollout_gt[:, -1]) # compute losses at final step of the rollout mse_final_step = criterionMSE( state_rollout_pred[:, -1], state_rollout_gt[:, -1]) l2_final_step = torch.norm(state_pred_err[:, -1], dim=-1).mean() l1_final_step = l1Loss(state_rollout_pred[:, -1], state_rollout_gt[:, -1]) loss_container['mse'] = loss_mse loss_container['l1'] = loss_l1 loss_container['mse_final_step'] = mse_final_step loss_container['l1_final_step'] = l1_final_step loss_container['l2_final_step'] = l2_final_step loss_container['l2'] = loss_l2 loss_container['smooth_l1'] = loss_smoothl1 loss_container[ 'smooth_l1_final_step'] = loss_smoothl1_final_step # compute the loss loss = 0 for key, val in config['loss_function'].items(): if val['enabled']: loss += loss_container[key] * val['weight'] loss_container['loss'] = loss for key, val in loss_container.items(): if not key in average_meter_container: average_meter_container[key] = AverageMeter() average_meter_container[key].update(val.item(), B) step_duration_meter.update(time.time() - step_start_time) if phase == 'train': optimizer.zero_grad() loss.backward() optimizer.step() if (i % config['train']['log_per_iter'] == 0) or (global_iteration % config['train']['log_per_iter'] == 0): log = '%s [%d/%d][%d/%d] LR: %.6f' % ( phase, epoch, config['train']['n_epoch'], i, data_n_batches[phase], get_lr(optimizer)) log += ', l2: %.6f' % (loss_container['l2'].item()) log += ', l2_final_step: %.6f' % ( loss_container['l2_final_step'].item()) log += ', step time %.6f' % (step_duration_meter.avg) step_duration_meter.reset() print(log) # log data to tensorboard # only do it once we have reached 100 iterations if global_iteration > 100: writer.add_scalar("Params/learning rate", get_lr(optimizer), global_iteration) writer.add_scalar("Loss_train/%s" % (phase), loss.item(), global_iteration) for loss_type, loss_obj in loss_container.items(): plot_name = "Loss/%s/%s" % (loss_type, phase) writer.add_scalar(plot_name, loss_obj.item(), counters[phase]) if phase == 'train' and global_iteration % config['train'][ 'ckp_per_iter'] == 0: save_model( model_dy, '%s/net_dy_epoch_%d_iter_%d' % (train_dir, epoch, i)) log = '%s [%d/%d] Loss: %.6f, Best valid: %.6f' % ( phase, epoch, config['train']['n_epoch'], average_meter_container[valid_loss_type].avg, best_valid_loss) print(log) # record all average_meter losses for key, meter in average_meter_container.items(): writer.add_scalar("AvgMeter/%s/%s" % (key, phase), meter.avg, epoch) if phase == "train": if (scheduler is not None) and ( config['train']['lr_scheduler']['type'] == "StepLR"): scheduler.step() if phase == 'valid': if (scheduler is not None) and ( config['train']['lr_scheduler']['type'] == "ReduceLROnPlateau"): scheduler.step( average_meter_container[valid_loss_type].avg) if average_meter_container[ valid_loss_type].avg < best_valid_loss: best_valid_loss = average_meter_container[ valid_loss_type].avg training_stats['epoch'] = epoch training_stats['global_iteration'] = counters['valid'] save_yaml(training_stats, training_stats_file) save_model(model_dy, '%s/net_best_dy' % (train_dir)) writer.flush() # flush SummaryWriter events to disk except KeyboardInterrupt: # save network if we have a keyboard interrupt save_model( model_dy, '%s/net_dy_epoch_%d_keyboard_interrupt' % (train_dir, epoch_counter_external)) writer.flush() # flush SummaryWriter events to disk
def train_dynamics(config, train_dir, # str: directory to save output multi_episode_dict, # multi_episode_dict ): use_precomputed_keypoints = config['dataset']['visual_observation']['enabled'] and config['dataset']['visual_observation']['descriptor_keypoints'] # set random seed for reproduction set_seed(config['train']['random_seed']) st_epoch = config['train']['resume_epoch'] if config['train']['resume_epoch'] > 0 else 0 tee = Tee(os.path.join(train_dir, 'train_st_epoch_%d.log' % st_epoch), 'w') tensorboard_dir = os.path.join(train_dir, "tensorboard") if not os.path.exists(tensorboard_dir): os.makedirs(tensorboard_dir) writer = SummaryWriter(log_dir=tensorboard_dir) # save the config save_yaml(config, os.path.join(train_dir, "config.yaml")) action_function = ActionFunctionFactory.function_from_config(config) observation_function = ObservationFunctionFactory.function_from_config(config) datasets = {} dataloaders = {} data_n_batches = {} for phase in ['train', 'valid']: print("Loading data for %s" % phase) datasets[phase] = MultiEpisodeDataset(config, action_function=action_function, observation_function=observation_function, episodes=multi_episode_dict, phase=phase) dataloaders[phase] = DataLoader( datasets[phase], batch_size=config['train']['batch_size'], shuffle=True if phase == 'train' else False, num_workers=config['train']['num_workers'], drop_last=True) data_n_batches[phase] = len(dataloaders[phase]) use_gpu = torch.cuda.is_available() # compute normalization parameters if not starting from pre-trained network . . . ''' define model for dynamics prediction ''' model_dy = build_visual_dynamics_model(config) K = config['vision_net']['num_ref_descriptors'] print("model_dy.vision_net._reference_descriptors.shape", model_dy.vision_net._ref_descriptors.shape) print("model_dy.vision_net.descriptor_dim", model_dy.vision_net.descriptor_dim) print("model_dy #params: %d" % count_trainable_parameters(model_dy)) camera_name = config['vision_net']['camera_name'] W = config['env']['rgbd_sensors']['sensor_list'][camera_name]['width'] H = config['env']['rgbd_sensors']['sensor_list'][camera_name]['height'] diag = np.sqrt(W**2 + H**2) # use this to scale the loss # sample reference descriptors unless using precomputed keypoints if not use_precomputed_keypoints: # sample reference descriptors episode_names = list(datasets["train"].episode_dict.keys()) episode_names.sort() episode_name = episode_names[0] episode = datasets["train"].episode_dict[episode_name] episode_idx = 0 camera_name = config["vision_net"]["camera_name"] image_data = episode.get_image_data(camera_name, episode_idx) des_img = torch.Tensor(image_data['descriptor']) mask_img = torch.Tensor(image_data['mask']) ref_descriptor_dict = sample_descriptors(des_img, mask_img, config['vision_net']['num_ref_descriptors']) model_dy.vision_net._ref_descriptors.data = ref_descriptor_dict['descriptors'] model_dy.vision_net.reference_image = image_data['rgb'] model_dy.vision_net.reference_indices = ref_descriptor_dict['indices'] else: metadata_file = os.path.join(get_data_root(), config['dataset']['descriptor_keypoints_dir'], 'metadata.p') descriptor_metadata = load_pickle(metadata_file) # [32, 2] ref_descriptors = torch.Tensor(descriptor_metadata['ref_descriptors']) # [K, 2] ref_descriptors = ref_descriptors[:K] model_dy.vision_net._ref_descriptors.data = ref_descriptors model_dy.vision_net._ref_descriptors_metadata = descriptor_metadata # this is just a sanity check assert model_dy.vision_net.num_ref_descriptors == K print("reference_descriptors", model_dy.vision_net._ref_descriptors) # criterion criterionMSE = nn.MSELoss() l1Loss = nn.L1Loss() # optimizer params = model_dy.parameters() lr = float(config['train']['lr']) optimizer = optim.Adam(params, lr=lr, betas=(config['train']['adam_beta1'], 0.999)) # setup scheduler sc = config['train']['lr_scheduler'] scheduler = ReduceLROnPlateau(optimizer, mode='min', factor=sc['factor'], patience=sc['patience'], threshold_mode=sc['threshold_mode'], cooldown= sc['cooldown'], verbose=True) if use_gpu: print("using gpu") model_dy = model_dy.cuda() print("model_dy.vision_net._ref_descriptors.device", model_dy.vision_net._ref_descriptors.device) print("model_dy.vision_net #params: %d" %(count_trainable_parameters(model_dy.vision_net))) best_valid_loss = np.inf global_iteration = 0 epoch_counter_external = 0 try: for epoch in range(st_epoch, config['train']['n_epoch']): phases = ['train', 'valid'] epoch_counter_external = epoch writer.add_scalar("Training Params/epoch", epoch, global_iteration) for phase in phases: model_dy.train(phase == 'train') meter_loss_rmse = AverageMeter() step_duration_meter = AverageMeter() # bar = ProgressBar(max_value=data_n_batches[phase]) loader = dataloaders[phase] for i, data in enumerate(loader): step_start_time = time.time() global_iteration += 1 with torch.set_grad_enabled(phase == 'train'): n_his, n_roll = config['train']['n_history'], config['train']['n_rollout'] n_samples = n_his + n_roll if DEBUG: print("global iteration: %d" %(global_iteration)) # visual_observations = data['visual_observations'] visual_observations_list = data['visual_observations_list'] observations = data['observations'] actions = data['actions'] if use_gpu: observations = observations.cuda() actions = actions.cuda() # states, actions = data assert actions.size(1) == n_samples B = actions.size(0) loss_mse = 0. # compute the output of the visual model for all timesteps visual_model_output_list = [] for visual_obs in visual_observations_list: # visual_obs is a dict containing observation for a single # time step (of course across a batch however) # visual_obs[<camera_name>]['rgb_tensor'] has shape [B, 3, H, W] # probably need to cast input to cuda dynamics_net_input = None if use_precomputed_keypoints: # note precomputed descriptors stored on disk are of size # K = 32. We need to trim it down to the appropriate size # [B, K_disk, 2] where K_disk is num keypoints on disk keypoints = visual_obs[camera_name]['descriptor_keypoints'] # [B, 32, 2] where K is num keypoints keypoints = keypoints[:,:K] if DEBUG: print("keypoints.shape", keypoints.shape) dynamics_net_input = keypoints.flatten(start_dim=1) else: out_dict = model_dy.vision_net.forward(visual_obs) # [B, vision_model_out_dim] dynamics_net_input = out_dict['dynamics_net_input'] visual_model_output_list.append(dynamics_net_input) # concatenate this into a tensor # [B, n_samples, vision_model_out_dim] visual_model_output = torch.stack(visual_model_output_list, dim=1) # cast this to float so it can be concatenated below visual_model_output = visual_model_output.type_as(observations) if DEBUG: print('visual_model_output.shape', visual_model_output.shape) print("observations.shape", observations.shape) print("actions.shape", actions.shape) # states is gotten by concatenating visual_observations and observations # [B, n_samples, vision_model_out_dim + obs_dim] states = torch.cat((visual_model_output, observations), dim=-1) # state_cur: B x n_his x state_dim state_cur = states[:, :n_his] if DEBUG: print("states.shape", states.shape) for j in range(n_roll): if DEBUG: print("n_roll j: %d" %(j)) state_des = states[:, n_his + j] # action_cur: B x n_his x action_dim action_cur = actions[:, j : j + n_his] if actions is not None else None # state_pred: B x state_dim # state_pred: B x state_dim input = {'observation': state_cur, 'action': action_cur, } if DEBUG: print("state_cur.shape", state_cur.shape) print("action_cur.shape", action_cur.shape) state_pred = model_dy.dynamics_net(input) # normalize by diag to ensure the loss is in [0,1] range loss_mse_cur = criterionMSE(state_pred/diag, state_des/diag) loss_mse += loss_mse_cur / n_roll # l1Loss loss_l1 = l1Loss(state_pred, state_des) # update state_cur # state_pred.unsqueeze(1): B x 1 x state_dim # state_cur: B x n_his x state_dim state_cur = torch.cat([state_cur[:, 1:], state_pred.unsqueeze(1)], 1) meter_loss_rmse.update(np.sqrt(loss_mse.item()), B) step_duration_meter.update(time.time() - step_start_time) if phase == 'train': optimizer.zero_grad() loss_mse.backward() optimizer.step() if (i % config['train']['log_per_iter'] == 0) or (global_iteration % config['train']['log_per_iter'] == 0): log = '%s [%d/%d][%d/%d] LR: %.6f' % ( phase, epoch, config['train']['n_epoch'], i, data_n_batches[phase], get_lr(optimizer)) log += ', rmse: %.6f (%.6f)' % ( np.sqrt(loss_mse.item()), meter_loss_rmse.avg) log += ', step time %.6f' %(step_duration_meter.avg) step_duration_meter.reset() print(log) # log data to tensorboard # only do it once we have reached 100 iterations if global_iteration > 100: writer.add_scalar("Params/learning rate", get_lr(optimizer), global_iteration) writer.add_scalar("Loss_MSE/%s" %(phase), loss_mse.item(), global_iteration) writer.add_scalar("L1/%s" %(phase), loss_l1.item(), global_iteration) writer.add_scalar("L1_fraction/%s" %(phase), loss_l1.item()/diag, global_iteration) writer.add_scalar("RMSE average loss/%s" %(phase), meter_loss_rmse.avg, global_iteration) if phase == 'train' and i % config['train']['ckp_per_iter'] == 0: save_model(model_dy, '%s/net_dy_epoch_%d_iter_%d' % (train_dir, epoch, i)) log = '%s [%d/%d] Loss: %.6f, Best valid: %.6f' % ( phase, epoch, config['train']['n_epoch'], meter_loss_rmse.avg, best_valid_loss) print(log) if phase == 'valid': if config['train']['lr_scheduler']['enabled']: scheduler.step(meter_loss_rmse.avg) # print("\nPhase == valid") # print("meter_loss_rmse.avg", meter_loss_rmse.avg) # print("best_valid_loss", best_valid_loss) if meter_loss_rmse.avg < best_valid_loss: best_valid_loss = meter_loss_rmse.avg save_model(model_dy, '%s/net_best_dy' % (train_dir)) writer.flush() # flush SummaryWriter events to disk except KeyboardInterrupt: # save network if we have a keyboard interrupt save_model(model_dy, '%s/net_dy_epoch_%d_keyboard_interrupt' % (train_dir, epoch_counter_external)) writer.flush() # flush SummaryWriter events to disk
def collect_episodes(config, output_dir=None, visualize=True, debug=False, run_from_thread=False, seed=None): # gets a random seed for each thread/process independently if seed is None: seed = np.random.RandomState().randint(0, 10000) set_seed(seed) if output_dir is None: output_dir = os.path.join(os.getcwd(), 'data') if not os.path.exists(output_dir): os.makedirs(output_dir) # save the config config_save_file = os.path.join(output_dir, 'config.yaml') save_yaml(config, config_save_file) # initialize config for DataCollector num_episodes = config['dataset']['num_episodes'] # record some metadata metadata = dict() metadata['episodes'] = dict() while (len(metadata['episodes']) < num_episodes): i = len(metadata['episodes']) if debug: input("Press Enter to continue...") print("\n") start_time = time.time() print("collecting episode %d of %d" % (i + 1, num_episodes)) name = "%s_idx_%d" % (get_current_YYYY_MM_DD_hh_mm_ss_ms(), i) n_his = config['train_dynamics']['n_history'] ic = generate_initial_condition( config=config, T_aug_enabled=True, n_his=n_his, randomize_velocity=True, randomize_sdf=True, randomize_color=True, ) env = DrakeMugsEnv(ic['config'], visualize=visualize) if debug: print("initial condition\n", ic) # set initial condition on environment if visualize: print("setting target realtime rate 1.0") env.simulator.set_target_realtime_rate(1.0) env.reset() context = env.get_mutable_context() env.set_object_position(context, ic['q_slider']) env.set_pusher_position(context, ic['q_pusher']) print("ic['action_sequence'].shape", ic['action_sequence'].shape) # simulate for 10 seconds to let the mug stabilize action_zero = env.get_zero_action() env.step(action_zero, dt=10.0) episode = collect_single_episode( env, action_seq=ic['action_sequence'])['episode_container'] # potentially discard it if the object didn't move during the data collection if len(episode._data['trajectory']) < 10: print("trajectory was too short, skipping") continue obs_start = episode._data['trajectory'][0]['observation'] obs_end = episode._data['trajectory'][-1]['observation'] q_slider_start = obs_start['slider']['position']['translation'] q_slider_end = obs_end['slider']['position']['translation'] dq_slider = obs_start['slider']['position']['translation'] - obs_end[ 'slider']['position']['translation'] if debug: print("len(episode._data['trajectory'])", len(episode._data['trajectory'])) print("q_slider_start", q_slider_start) print("q_slider_end", q_slider_end) print("dq_slider", dq_slider) print("np.linalg.norm(dq_slider)", np.linalg.norm(dq_slider)) pose_error = compute_pose_error(obs_start, obs_end) # if slider didn't move by at least 1 mm then discard this episode if (pose_error['position_error'] < 0.01) and (pose_error['angle_error_degrees'] < 10): print( "discarding episode since slider didn't move sufficiently far") continue print("saving to disk") metadata['episodes'][name] = dict() image_data_file = episode.save_images_to_hdf5(output_dir) non_image_data_file = episode.save_non_image_data_to_pickle(output_dir) print("output_dir:", output_dir) print("non_image_data.keys()", episode.non_image_data.keys()) metadata['episodes'][name]['non_image_data_file'] = non_image_data_file metadata['episodes'][name]['image_data_file'] = image_data_file print("done saving to disk") elapsed = time.time() - start_time print("single episode took: %.2f seconds" % (elapsed)) if not run_from_thread: save_yaml(metadata, os.path.join(output_dir, 'metadata.yaml')) print("Finished collecting episodes") return {'metadata': metadata}
def multiprocess_main(num_episodes=1000, num_threads=4): set_seed(500) # just randomly chosen start_time = time.time() config = load_yaml( os.path.join(get_project_root(), 'experiments/exp_20_mugs/config.yaml')) num_episodes_per_thread = math.ceil(num_episodes / num_threads) num_episodes = num_threads * num_episodes_per_thread # DATASET_NAME = "mugs_random_colors_%d" % (num_episodes) # DATASET_NAME = "single_mug_%d" # DATASET_NAME = "correlle_mug-small_single_color_%d" %(num_episodes) # DATASET_NAME = "single_corelle_mug_%d" %(num_episodes) # DATASET_NAME = "correlle_mug-small_many_colors_%d" %(num_episodes) DATASET_NAME = "correlle_mug-small_many_colors_random_%d" % (num_episodes) # OUTPUT_DIR = os.path.join(get_data_root(), 'sandbox', DATASET_NAME) OUTPUT_DIR = os.path.join(get_data_ssd_root(), 'dataset', DATASET_NAME) print("OUTPUT_DIR:", OUTPUT_DIR) output_dir = OUTPUT_DIR if not os.path.exists(output_dir): os.makedirs(output_dir) 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) q = Queue() process_list = [] for i in range(num_threads): p = Process(target=f, args=(q, )) p.start() process_list.append(p) metadata = {'episodes': {}} for p in process_list: while p.is_alive(): p.join(timeout=1) # empty out the queue while not q.empty(): out = q.get() metadata['episodes'].update(out['metadata']['episodes']) # double check for p in process_list: p.join() time.sleep(1.0) print("All threads joined") elapsed = time.time() - start_time # collect the metadata.yaml files while not q.empty(): out = q.get() metadata['episodes'].update(out['metadata']['episodes']) save_yaml(metadata, os.path.join(OUTPUT_DIR, 'metadata.yaml')) print("Generating and saving dataset to disk took %d seconds" % (int(elapsed)))
def mpc_w_learned_dynamics(config, train_dir, mpc_dir, state_dict_path=None, keypoint_observation=False): # set random seed for reproduction set_seed(config['train']['random_seed']) tee = Tee(os.path.join(mpc_dir, 'mpc.log'), 'w') print(config) use_gpu = torch.cuda.is_available() ''' model ''' if config['dynamics']['model_type'] == 'mlp': model_dy = DynaNetMLP(config) else: raise AssertionError("Unknown model type %s" % config['dynamics']['model_type']) # print model #params print("model #params: %d" % count_trainable_parameters(model_dy)) if state_dict_path is None: if config['mpc']['mpc_dy_epoch'] == -1: state_dict_path = os.path.join(train_dir, 'net_best_dy.pth') else: state_dict_path = os.path.join( train_dir, 'net_dy_epoch_%d_iter_%d.pth' % \ (config['mpc']['mpc_dy_epoch'], config['mpc']['mpc_dy_iter'])) print("Loading saved ckp from %s" % state_dict_path) model_dy.load_state_dict(torch.load(state_dict_path)) model_dy.eval() if use_gpu: model_dy.cuda() criterionMSE = nn.MSELoss() # generate action/observation functions action_function = ActionFunctionFactory.function_from_config(config) observation_function = ObservationFunctionFactory.function_from_config( config) # planner planner = planner_from_config(config) ''' env ''' # set up goal obs_goals = np.array([[ 262.9843, 267.3102, 318.9369, 351.1229, 360.2048, 323.5128, 305.6385, 240.4460, 515.4230, 347.8708 ], [ 381.8694, 273.6327, 299.6685, 331.0925, 328.7724, 372.0096, 411.0972, 314.7053, 517.7299, 268.4953 ], [ 284.8728, 275.7985, 374.0677, 320.4990, 395.4019, 275.4633, 306.2896, 231.4310, 507.0849, 312.4057 ], [ 313.1638, 271.4258, 405.0255, 312.2325, 424.7874, 266.3525, 333.6973, 225.7708, 510.1232, 305.3802 ], [ 308.6859, 270.9629, 394.2789, 323.2781, 419.7905, 280.1602, 333.8901, 228.1624, 519.1964, 321.5318 ], [ 386.8067, 284.8947, 294.2467, 323.2223, 313.3221, 368.9970, 405.9415, 330.9298, 495.9970, 268.9920 ], [ 432.0219, 299.6021, 340.8581, 339.4676, 360.2354, 384.5515, 451.4394, 345.2190, 514.6357, 291.2043 ], [ 351.3389, 264.5325, 267.5279, 318.2321, 293.7460, 360.0423, 378.4428, 306.9586, 516.4390, 259.7810 ], [ 521.1902, 254.0693, 492.7884, 349.7861, 539.6320, 364.5190, 569.2258, 268.8824, 506.9431, 286.9752 ], [ 264.8554, 275.9547, 338.1317, 345.3435, 372.7012, 308.4648, 299.3454, 239.9245, 506.2117, 373.8413 ]]) for mpc_idx in range(config['mpc']['num_episodes']): if keypoint_observation: mpc_episode_keypoint_observation(config, mpc_idx, model_dy, mpc_dir, planner, obs_goals[mpc_idx], action_function, observation_function, use_gpu=use_gpu) else: # not supported for now raise AssertionError("currently only support keypoint observation")
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_18_box_on_side/config.yaml')) env_config['env']['observation']['depth_int16'] = True n_history = config['train']['n_history'] # 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 = DrakePusherSliderEnv(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 = DrakePusherSliderEnv(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() initial_cond = get_initial_state() 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)) # keypoints_W # color = [1, 0, 0] # meshcat_utils.visualize_points(vis=vis, # name="keypoints_W", # pts=z_keypoints_init_W, # color=color, # size=0.02, # ) # rollout single action sequence using the simulator gt_rollout_data = env_utils.rollout_action_sequence( env, initial_cond['action_sequence'].cpu().numpy()) env_obs_rollout_gt = gt_rollout_data['observations'] gt_rollout_episode = gt_rollout_data['episode_reader'] action_state_gt = mpc_input_builder.get_action_state_tensors( start_idx=0, num_timesteps=N, episode=gt_rollout_episode) state_rollout_gt = action_state_gt['states'] action_rollout_gt = action_state_gt['actions'] z_object_rollout_gt = model.compute_z_state( state_rollout_gt)['z_object_flat'] print('state_rollout_gt.shape', state_rollout_gt.shape) print("z_object_rollout_gt.shape", z_object_rollout_gt.shape) # using the vision model to get "goal" keypoints z_object_goal = goal_func(env_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 = 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(env_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_config['mpc']['mppi']['terminal_cost_only'] = TERMINAL_COST_ONLY planner_config['mpc']['random_shooting'][ 'terminal_cost_only'] = TERMINAL_COST_ONLY 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 elif USE_SHORT_HORIZON_MPC: n_look_ahead = min(MPC_HORIZON, N - counter) if n_look_ahead == 0: break start_idx = counter end_idx = counter + n_look_ahead print("start_idx:", start_idx) print("end_idx:", end_idx) # 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 SEED_WITH_NOMINAL_ACTIONS: action_seq_rollout_init = action_seq_gt_torch[ start_idx:end_idx] else: if mpc_out is not None: action_seq_rollout_init = mpc_out['action_seq'][1:] print("action_seq_rollout_init.shape", action_seq_rollout_init.shape) if action_seq_rollout_init.shape[0] < n_look_ahead: num_steps = n_look_ahead - action_seq_rollout_init.shape[ 0] action_seq_zero = torch.zeros([num_steps, 2]) action_seq_rollout_init = torch.cat( (action_seq_rollout_init, action_seq_zero), dim=0) print("action_seq_rollout_init.shape", action_seq_rollout_init.shape) 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) if action_seq_rollout_init is not None: n_look_ahead = action_seq_rollout_init.shape[0] obs_goal = None print("z_object_rollout_gt.shape", z_object_rollout_gt.shape) if TRAJECTORY_GOAL: obs_goal = z_object_rollout_gt[start_idx:end_idx] print("n_look_ahead", n_look_ahead) print("obs_goal.shape", obs_goal.shape) # add the final goal state on as needed if obs_goal.shape[0] < n_look_ahead: obs_goal_final = z_object_rollout_gt[-1].unsqueeze(0) num_repeat = n_look_ahead - obs_goal.shape[0] obs_goal_final_expand = obs_goal_final.expand( [num_repeat, -1]) obs_goal = torch.cat((obs_goal, obs_goal_final_expand), dim=0) else: obs_goal = z_object_rollout_gt[-1] obs_goal = torch_utils.cast_to_numpy(obs_goal) print("obs_goal.shape", obs_goal.shape) seed = 1 set_seed(seed) mpc_out = planner.trajectory_optimization( state_cur=z_cur, action_his=action_his, obs_goal=obs_goal, 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, add_grid_action_samples=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:] pose_error = compute_pose_error(env_obs_rollout_gt[-1], obs_cur) print("position_error: %.3f" % (pose_error['position_error'])) print("angle error degrees: %.3f" % (pose_error['angle_error_degrees'])) obs_final = env.get_observation() pose_error = compute_pose_error(env_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 eval_dynamics(config, train_dir, eval_dir, state_dict_path=None, keypoint_observation=False, debug=False, render_human=False): # set random seed for reproduction set_seed(config['train']['random_seed']) tee = Tee(os.path.join(eval_dir, 'eval.log'), 'w') print(config) use_gpu = torch.cuda.is_available() ''' model ''' model_dy = DynaNetMLP(config) # print model #params print("model #params: %d" % count_trainable_parameters(model_dy)) if state_dict_path is None: if config['eval']['eval_dy_epoch'] == -1: state_dict_path = os.path.join(train_dir, 'net_best_dy.pth') else: state_dict_path = os.path.join( train_dir, 'net_dy_epoch_%d_iter_%d.pth' % \ (config['eval']['eval_dy_epoch'], config['eval']['eval_dy_iter'])) print("Loading saved ckp from %s" % state_dict_path) model_dy.load_state_dict(torch.load(state_dict_path)) model_dy.eval() if use_gpu: model_dy.cuda() criterionMSE = nn.MSELoss() bar = ProgressBar() st_idx = config['eval']['eval_st_idx'] ed_idx = config['eval']['eval_ed_idx'] # load the data episodes = load_episodes_from_config(config) # generate action/observation functions 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="valid") episode_names = dataset.get_episode_names() episode_names.sort() num_episodes = None # for backwards compatibility if "num_episodes" in config["eval"]: num_episodes = config["eval"]["num_episodes"] else: num_episodes = 10 episode_list = [] if debug: episode_list = [episode_names[0]] else: episode_list = episode_names[:num_episodes] for roll_idx, episode_name in enumerate(episode_list): print("episode_name", episode_name) if keypoint_observation: eval_episode_keypoint_observations(config, dataset, episode_name, roll_idx, model_dy, eval_dir, start_idx=9, n_prediction=30, render_human=render_human) else: eval_episode(config, dataset, episode_name, roll_idx, model_dy, eval_dir, start_idx=9, n_prediction=30, render_human=render_human)
def train_autoencoder(config, train_dir, ckp_dir=None, multi_episode_dict=None, type=None, # ["SpatialAutoencoder", . . .] ): assert multi_episode_dict is not None if ckp_dir is None: ckp_dir = os.path.join(train_dir, 'checkpoints') if not os.path.exists(ckp_dir): os.makedirs(ckp_dir) tensorboard_dir = os.path.join(train_dir, "tensorboard") if not os.path.exists(tensorboard_dir): os.makedirs(tensorboard_dir) images_dir = os.path.join(train_dir, 'images') if not os.path.exists(images_dir): os.makedirs(images_dir) # save the config save_yaml(config, os.path.join(train_dir, 'config.yaml')) # set random seed for reproduction set_seed(config['train_autoencoder']['random_seed']) camera_names = [config['perception']['camera_name']] model = None image_preprocess_func = None if type == "SpatialAutoencoder": model = SpatialAutoencoder.from_global_config(config) image_preprocess_func = functools.partial(spatial_autoencoder_image_preprocessing, H_in=model.input_image_shape[0], W_in=model.input_image_shape[1], H_out=model.output_image_shape[0], W_out=model.output_image_shape[1]) elif type == "ConvolutionalAutoencoder": model = ConvolutionalAutoencoder.from_global_config(config) image_preprocess_func = AutoencoderImagePreprocessFunctionFactory.convolutional_autoencoder(config) else: raise ValueError("unknown model type: %s" % (type)) writer = SummaryWriter(log_dir=tensorboard_dir) # only use images from this specific config ### data datasets = {} dataloaders = {} for phase in ['train', 'valid']: datasets[phase] = AutoencoderImageDataset(config, phase=phase, episodes=multi_episode_dict, camera_names=camera_names, image_preprocess_func=image_preprocess_func) dataloaders[phase] = DataLoader( datasets[phase], batch_size=config['train_autoencoder']['batch_size'], shuffle=True if phase == 'train' else False, num_workers=config['train_autoencoder']['batch_size']) use_gpu = torch.cuda.is_available() params = model.parameters() optimizer = optim.Adam( params, lr=float(config['train_autoencoder']['lr']), betas=(config['train_autoencoder']['adam_beta1'], 0.999)) scheduler = None if config['train_autoencoder']['lr_scheduler']['enabled']: scheduler = ReduceLROnPlateau( optimizer, 'min', factor=0.6, patience=2, verbose=True) if use_gpu: model = model.cuda() best_valid_loss = np.inf global_iteration = 0 log_fout = open(os.path.join(ckp_dir, 'log.txt'), 'w') # criterion criterionMSE = nn.MSELoss() # a little test if False: data = datasets['train'][0] print(data.keys()) print("data['target_tensor'].shape", data['target_tensor'].shape) print("data['target_mask'].shape", data['target_mask'].shape) fig = plt.figure() ax = fig.subplots(2) target_img = data['target'] print("target_img.dtype", target_img.dtype) ax[0].imshow(data['input']) ax[1].imshow(data['target'], cmap='gray', vmin=0, vmax=255) plt.show() quit() # a little test if False: data = datasets['train'][0] print(data.keys()) print("data['target_tensor'].shape", data['target_tensor'].shape) print("data['target_mask'].shape", data['target_mask'].shape) fig = plt.figure() ax = fig.subplots(2) target_img = data['target'] target_tensor = data['target_tensor'].unsqueeze(0) target_tensor_np = torch_utils.convert_torch_image_to_numpy(target_tensor).squeeze() print("target_img.dtype", target_img.dtype) ax[0].imshow(target_img) ax[1].imshow(target_tensor_np) plt.show() quit() counters = {'train': 0, 'valid': 0} n_epoch = config['train_autoencoder']['n_epoch'] for epoch in range(n_epoch): phases = ['train', 'valid'] writer.add_scalar("Training Params/epoch", epoch, global_iteration) for phase in phases: model.train(phase == 'train') meter_loss = AverageMeter() loader = dataloaders[phase] bar = ProgressBar(max_value=len(loader)) step_duration_meter = AverageMeter() epoch_start_time = time.time() prev_time = time.time() print("\n\n") for i, data in bar(enumerate(loader)): loss_container = dict() # store the losses for this step counters[phase] += 1 with torch.set_grad_enabled(phase == 'train'): input = data['input_tensor'] target = data['target_tensor'] if use_gpu: input = input.cuda() target = target.cuda() out = model(input) target_pred = out['output'] # print("target.shape", target.shape) # print("target_pred.shape", target_pred.shape) # reconstruction loss l2_recon = criterionMSE(target, target_pred) loss_container['l2_recon'] = l2_recon # loss_masked # [B, H', W'] mask = data['target_mask'].to(target.device) mask_idx = mask > 0 # convert to BHWC ordering so we can directly index target_masked = target.permute(0, 2, 3, 1)[mask_idx] target_pred_masked = target_pred.permute(0, 2, 3, 1)[mask_idx] # print('target_masked.shape', target_masked.shape) # print("target_pred_masked.shape", target_pred_masked.shape) l2_recon_masked = criterionMSE(target_masked, target_pred_masked) loss_container['l2_recon_masked'] = l2_recon_masked # compute the loss loss = 0 for key, val in config['train_autoencoder']['loss_function'].items(): if val['enabled']: loss += loss_container[key] * val['weight'] meter_loss.update(loss.item()) step_duration_meter.update(time.time() - prev_time) prev_time = time.time() if phase == 'train': optimizer.zero_grad() loss.backward() nn.utils.clip_grad_norm_(params, 1) optimizer.step() if global_iteration > 100: writer.add_scalar("Params/learning rate", get_lr(optimizer), global_iteration) # writer.add_scalar("Loss/%s" % (phase), loss.item(), global_iteration) writer.add_scalar("Loss_train/%s" % (phase), loss.item(), counters[phase]) for loss_type, loss_obj in loss_container.items(): plot_name = "Loss/%s/%s" % (loss_type, phase) writer.add_scalar(plot_name, loss_obj.item(), counters[phase]) if i % config['train_autoencoder']['log_per_iter'] == 0: log = '%s [%d/%d][%d/%d] LR: %.6f, Loss: %.6f (%.6f)' % ( phase, epoch, n_epoch, i, len(loader), get_lr(optimizer), loss.item(), meter_loss.avg) log += ', step time %.6f' % (step_duration_meter.avg) step_duration_meter.reset() print(log) log_fout.write(log + '\n') log_fout.flush() if phase == 'train' and i % config['train_autoencoder']['ckp_per_iter'] == 0: torch.save( model.state_dict(), '%s/net_kp_epoch_%d_iter_%d.pth' % (ckp_dir, epoch, i)) if i % config['train_autoencoder']['img_save_per_iter'] == 0: nrows = 4 ncols = 2 fig_width = 5 B, _, H, W = target.shape fig_height = fig_width * ((nrows * H) / (ncols * W)) figsize = (fig_width, fig_height) fig = plt.figure(figsize=figsize) ax = fig.subplots(nrows=nrows, ncols=ncols) target_np = torch_utils.convert_torch_image_to_numpy(target) target_pred_np = torch_utils.convert_torch_image_to_numpy(target_pred) for n in range(nrows): ax[n, 0].imshow(target_np[n]) ax[n, 1].imshow(target_pred_np[n]) save_file = os.path.join(images_dir, '%s_epoch_%d_iter_%d.png' %(phase, epoch, i)) fig.savefig(save_file) plt.close(fig) writer.flush() # flush SummaryWriter events to disk global_iteration += 1 log = '%s [%d/%d] Loss: %.6f, Best valid: %.6f' % ( phase, epoch, n_epoch, meter_loss.avg, best_valid_loss) print(log) print("Epoch Duration:", time.time() - epoch_start_time) log_fout.write(log + '\n') log_fout.flush() if phase == 'valid': if scheduler is not None: scheduler.step(meter_loss.avg) if meter_loss.avg < best_valid_loss: best_valid_loss = meter_loss.avg torch.save(model.state_dict(), '%s/net_best.pth' % ckp_dir) log_fout.close()
def train_transporter( config, train_dir, ckp_dir=None, multi_episode_dict=None, ): assert multi_episode_dict is not None if ckp_dir is None: ckp_dir = os.path.join(train_dir) if not os.path.exists(ckp_dir): os.makedirs(ckp_dir) tensorboard_dir = os.path.join(train_dir, "tensorboard") if not os.path.exists(tensorboard_dir): os.makedirs(tensorboard_dir) images_dir = os.path.join(train_dir, 'images') if not os.path.exists(images_dir): os.makedirs(images_dir) # save the config save_yaml(config, os.path.join(train_dir, 'config.yaml')) # set random seed for reproduction set_seed(config['train_transporter']['random_seed']) writer = SummaryWriter(log_dir=tensorboard_dir) # only use images from this specific config camera_names = [config['perception']['camera_name']] ### data datasets = {} dataloaders = {} for phase in ['train', 'valid']: datasets[phase] = ImageTupleDataset(config, phase=phase, episodes=multi_episode_dict, tuple_size=2, camera_names=camera_names) dataloaders[phase] = DataLoader( datasets[phase], batch_size=config['train_transporter']['batch_size'], shuffle=True if phase == 'train' else False, num_workers=config['train_transporter']['batch_size']) use_gpu = torch.cuda.is_available() crop_enabled = datasets['train'].crop_enabled rgb_tensor_key = None if crop_enabled: rgb_image_key = "rgb_crop" rgb_tensor_key = "rgb_crop_tensor" else: rgb_image_key = "rgb_masked_scaled" rgb_tensor_key = "rgb_masked_scaled_tensor" if False: dataset = datasets["train"] dataset_size = len(dataset) print("len(dataset)", len(dataset)) print("len(dataset._image_dataset)", len(dataset._image_dataset)) print("len(dataset['valid'])", len(datasets['valid'])) print("len(dataset['train'])", len(datasets['train'])) print("dataset.crop_enabled", dataset.crop_enabled) data = dataset[0] print("data.keys()", data.keys()) print("data[0].keys()", data[0].keys()) # rgb_crop_tensor = data[0]['rgb_crop_tensor'] # print("rgb_crop_tensor.max()", rgb_crop_tensor.max()) # print("rgb_crop_tensor.min()", rgb_crop_tensor.min()) # # rgb_image = data[0]['rgb_masked_scaled'] # print("rgb_crop.dtype", rgb_image.dtype) # print("rgb_image.shape", rgb_image.shape) rgb_image = data[0][rgb_image_key] rgb_tensor = data[0][rgb_tensor_key] print("rgb_image.shape", rgb_image.shape) print("rgb_tensor.shape", rgb_tensor.shape) plt.figure() # plt.imshow(rgb_image) plt.imshow(data[0][rgb_image_key]) plt.show() quit() # ### model model_kp = Transporter(config, use_gpu=use_gpu) print("model_kp #params: %d" % count_parameters(model_kp)) if config['train_transporter']['resume_epoch'] >= 0: model_kp_path = os.path.join( ckp_dir, 'net_kp_epoch_%d_iter_%d.pth' % (config['train_transporter']['resume_epoch'], config['train_transporter']['resume_iter'])) print("Loading saved ckp from %s" % model_kp_path) model_kp.load_state_dict(torch.load(model_kp_path)) # criterion criterionMSE = nn.MSELoss() # optimizer params = model_kp.parameters() optimizer = optim.Adam(params, lr=float(config['train_transporter']['lr']), betas=(config['train_transporter']['adam_beta1'], 0.999)) scheduler = ReduceLROnPlateau(optimizer, 'min', factor=0.6, patience=2, verbose=True) if use_gpu: model_kp = model_kp.cuda() best_valid_loss = np.inf global_iteration = 0 log_fout = open(os.path.join(ckp_dir, 'log.txt'), 'w') n_epoch = config['train_transporter']['n_epoch'] for epoch in range(n_epoch): phases = ['train', 'valid'] writer.add_scalar("Training Params/epoch", epoch, global_iteration) for phase in phases: model_kp.train(phase == 'train') meter_loss = AverageMeter() loader = dataloaders[phase] bar = ProgressBar(max_value=len(loader)) for i, data in bar(enumerate(loader)): with torch.set_grad_enabled(phase == 'train'): src = data[0][rgb_tensor_key] des = data[1][rgb_tensor_key] if use_gpu: src = src.cuda() des = des.cuda() des_pred = model_kp(src, des) # reconstruction loss loss = criterionMSE(des_pred, des) meter_loss.update(loss.item(), src.size(0)) if phase == 'train': optimizer.zero_grad() loss.backward() optimizer.step() if global_iteration > 100: writer.add_scalar("Params/learning rate", get_lr(optimizer), global_iteration) writer.add_scalar("Loss/%s" % (phase), loss.item(), global_iteration) if i % config['train_transporter']['log_per_iter'] == 0: log = '%s [%d/%d][%d/%d] LR: %.6f, Loss: %.6f (%.6f)' % ( phase, epoch, n_epoch, i, len(loader), get_lr(optimizer), loss.item(), meter_loss.avg) print() print(log) log_fout.write(log + '\n') log_fout.flush() if phase == 'train' and i % config['train_transporter'][ 'ckp_per_iter'] == 0: torch.save( model_kp.state_dict(), '%s/net_kp_epoch_%d_iter_%d.pth' % (ckp_dir, epoch, i)) # compute some images and draw them if global_iteration % config['train_transporter'][ 'image_per_iter'] == 0: with torch.no_grad(): kp = model_kp.predict_keypoint(des) heatmap = model_kp.keypoint_to_heatmap( kp, inv_std=config['perception']['inv_std']) images = visualize_transporter_output( des=des, des_pred=des_pred, heatmap=heatmap, kp=kp) print("images[0].shape", images[0].shape) save_img = np.concatenate(images[:4], axis=0) print("save_img.dtype", save_img.dtype) print("save_img.shape", save_img.shape) save_file = os.path.join( images_dir, '%s_epoch_%d_iter_%d.png' % (phase, epoch, i)) cv2.imwrite(save_file, save_img) pass writer.flush() # flush SummaryWriter events to disk global_iteration += 1 log = '%s [%d/%d] Loss: %.6f, Best valid: %.6f' % ( phase, epoch, n_epoch, meter_loss.avg, best_valid_loss) print(log) log_fout.write(log + '\n') log_fout.flush() if phase == 'valid': scheduler.step(meter_loss.avg) if meter_loss.avg < best_valid_loss: best_valid_loss = meter_loss.avg torch.save(model_kp.state_dict(), '%s/net_best.pth' % ckp_dir) log_fout.close()
def train_dynamics(config, data_path, train_dir): # access dict values as attributes config = edict(config) # set random seed for reproduction set_seed(config.train.random_seed) st_epoch = config.train.resume_epoch if config.train.resume_epoch > 0 else 0 tee = Tee(os.path.join(train_dir, 'train_st_epoch_%d.log' % st_epoch), 'w') print(config) datasets = {} dataloaders = {} data_n_batches = {} for phase in ['train', 'valid']: print("Loading data for %s" % phase) datasets[phase] = MultiEpisodeDataset(config, data_path, phase=phase) dataloaders[phase] = DataLoader( datasets[phase], batch_size=config.train.batch_size, shuffle=True if phase == 'train' else False, num_workers=config.train.num_workers) data_n_batches[phase] = len(dataloaders[phase]) use_gpu = torch.cuda.is_available() ''' define model for dynamics prediction ''' model_dy = DynaNetMLP(config) print("model_dy #params: %d" % count_trainable_parameters(model_dy)) if config.train.resume_epoch >= 0: # if resume from a pretrained checkpoint model_dy_path = os.path.join( train_dir, 'net_dy_epoch_%d_iter_%d.pth' % ( config.train.resume_epoch, config.train.resume_iter)) print("Loading saved ckp from %s" % model_dy_path) model_dy.load_state_dict(torch.load(model_dy_path)) # criterion criterionMSE = nn.MSELoss() # optimizer params = model_dy.parameters() optimizer = optim.Adam(params, lr=config.train.lr, betas=(config.train.adam_beta1, 0.999)) scheduler = ReduceLROnPlateau(optimizer, 'min', factor=0.9, patience=10, verbose=True) if use_gpu: model_dy = model_dy.cuda() best_valid_loss = np.inf for epoch in range(st_epoch, config.train.n_epoch): phases = ['train', 'valid'] for phase in phases: model_dy.train(phase == 'train') meter_loss_rmse = AverageMeter() bar = ProgressBar(max_value=data_n_batches[phase]) loader = dataloaders[phase] for i, data in bar(enumerate(loader)): if use_gpu: if isinstance(data, list): data = [d.cuda() for d in data] else: data = data.cuda() with torch.set_grad_enabled(phase == 'train'): n_his, n_roll = config.train.n_history, config.train.n_rollout n_samples = n_his + n_roll if config.env.type in ['PusherSlider']: states, actions = data assert states.size(1) == n_samples B = states.size(0) loss_mse = 0. # state_cur: B x n_his x state_dim state_cur = states[:, :n_his] for j in range(n_roll): state_des = states[:, n_his + j] # action_cur: B x n_his x action_dim action_cur = actions[:, j : j + n_his] if actions is not None else None # state_pred: B x state_dim state_pred = model_dy(state_cur, action_cur) loss_mse_cur = criterionMSE(state_pred, state_des) loss_mse += loss_mse_cur / config.train.n_rollout # update state_cur state_cur = torch.cat([state_cur[:, 1:], state_pred.unsqueeze(1)], 1) meter_loss_rmse.update(np.sqrt(loss_mse.item()), B) if phase == 'train': optimizer.zero_grad() loss_mse.backward() optimizer.step() if i % config.train.log_per_iter == 0: log = '%s [%d/%d][%d/%d] LR: %.6f' % ( phase, epoch, config.train.n_epoch, i, data_n_batches[phase], get_lr(optimizer)) log += ', rmse: %.6f (%.6f)' % ( np.sqrt(loss_mse.item()), meter_loss_rmse.avg) print(log) if phase == 'train' and i % config.train.ckp_per_iter == 0: torch.save(model_dy.state_dict(), '%s/net_dy_epoch_%d_iter_%d.pth' % (train_dir, epoch, i)) log = '%s [%d/%d] Loss: %.6f, Best valid: %.6f' % ( phase, epoch, config.train.n_epoch, meter_loss_rmse.avg, best_valid_loss) print(log) if phase == 'valid': scheduler.step(meter_loss_rmse.avg) if meter_loss_rmse.avg < best_valid_loss: best_valid_loss = meter_loss_rmse.avg torch.save(model_dy.state_dict(), '%s/net_best_dy.pth' % (train_dir))
def f(): seed = np.random.RandomState().randint(0, 10000) # print(np.random.RandomState().randint(0, 10000)) set_seed(seed) print(np.random.rand())
def train_dynamics( config, train_dir, # str: directory to save output multi_episode_dict=None, spatial_descriptors_idx=None, metadata=None, spatial_descriptors_data=None, ): assert multi_episode_dict is not None # assert spatial_descriptors_idx is not None # set random seed for reproduction set_seed(config['train']['random_seed']) st_epoch = config['train'][ 'resume_epoch'] if config['train']['resume_epoch'] > 0 else 0 tee = Tee(os.path.join(train_dir, 'train_st_epoch_%d.log' % st_epoch), 'w') tensorboard_dir = os.path.join(train_dir, "tensorboard") if not os.path.exists(tensorboard_dir): os.makedirs(tensorboard_dir) writer = SummaryWriter(log_dir=tensorboard_dir) # save the config save_yaml(config, os.path.join(train_dir, "config.yaml")) if metadata is not None: save_pickle(metadata, os.path.join(train_dir, 'metadata.p')) if spatial_descriptors_data is not None: save_pickle(spatial_descriptors_data, os.path.join(train_dir, 'spatial_descriptors.p')) training_stats = dict() training_stats_file = os.path.join(train_dir, 'training_stats.yaml') # load the data action_function = ActionFunctionFactory.function_from_config(config) observation_function = ObservationFunctionFactory.function_from_config( config) datasets = {} dataloaders = {} data_n_batches = {} for phase in ['train', 'valid']: print("Loading data for %s" % phase) datasets[phase] = MultiEpisodeDataset( config, action_function=action_function, observation_function=observation_function, episodes=multi_episode_dict, phase=phase) dataloaders[phase] = DataLoader( datasets[phase], batch_size=config['train']['batch_size'], shuffle=True if phase == 'train' else False, num_workers=config['train']['num_workers'], drop_last=True) data_n_batches[phase] = len(dataloaders[phase]) use_gpu = torch.cuda.is_available() # compute normalization parameters if not starting from pre-trained network . . . ''' Build model for dynamics prediction ''' model_dy = build_dynamics_model(config) camera_name = config['vision_net']['camera_name'] # criterion criterionMSE = nn.MSELoss() l1Loss = nn.L1Loss() smoothL1 = nn.SmoothL1Loss() # optimizer params = model_dy.parameters() lr = float(config['train']['lr']) optimizer = optim.Adam(params, lr=lr, betas=(config['train']['adam_beta1'], 0.999)) # setup scheduler sc = config['train']['lr_scheduler'] scheduler = None if config['train']['lr_scheduler']['enabled']: if config['train']['lr_scheduler']['type'] == "ReduceLROnPlateau": scheduler = ReduceLROnPlateau(optimizer, mode='min', factor=sc['factor'], patience=sc['patience'], threshold_mode=sc['threshold_mode'], cooldown=sc['cooldown'], verbose=True) elif config['train']['lr_scheduler']['type'] == "StepLR": step_size = config['train']['lr_scheduler']['step_size'] gamma = config['train']['lr_scheduler']['gamma'] scheduler = StepLR(optimizer, step_size=step_size, gamma=gamma) else: raise ValueError("unknown scheduler type: %s" % (config['train']['lr_scheduler']['type'])) if use_gpu: print("using gpu") model_dy = model_dy.cuda() # print("model_dy.vision_net._ref_descriptors.device", model_dy.vision_net._ref_descriptors.device) # print("model_dy.vision_net #params: %d" %(count_trainable_parameters(model_dy.vision_net))) best_valid_loss = np.inf valid_loss_type = config['train']['valid_loss_type'] global_iteration = 0 counters = {'train': 0, 'valid': 0} epoch_counter_external = 0 loss = 0 index_map = get_object_and_robot_state_indices(config) object_state_indices = torch.LongTensor(index_map['object_indices']) robot_state_indices = torch.LongTensor(index_map['robot_indices']) object_state_shape = config['dataset']['object_state_shape'] try: for epoch in range(st_epoch, config['train']['n_epoch']): phases = ['train', 'valid'] epoch_counter_external = epoch writer.add_scalar("Training Params/epoch", epoch, global_iteration) for phase in phases: # only validate at a certain frequency if (phase == "valid") and ( (epoch % config['train']['valid_frequency']) != 0): continue model_dy.train(phase == 'train') average_meter_container = dict() step_duration_meter = AverageMeter() # bar = ProgressBar(max_value=data_n_batches[phase]) loader = dataloaders[phase] for i, data in enumerate(loader): loss_container = dict() # store the losses for this step step_start_time = time.time() global_iteration += 1 counters[phase] += 1 with torch.set_grad_enabled(phase == 'train'): n_his, n_roll = config['train']['n_history'], config[ 'train']['n_rollout'] n_samples = n_his + n_roll if DEBUG: print("global iteration: %d" % (global_iteration)) print("n_samples", n_samples) # [B, n_samples, obs_dim] observations = data['observations'] visual_observations_list = data[ 'visual_observations_list'] # [B, n_samples, action_dim] actions = data['actions'] B = actions.shape[0] if use_gpu: observations = observations.cuda() actions = actions.cuda() # compile the visual observations # compute the output of the visual model for all timesteps visual_model_output_list = [] for visual_obs in visual_observations_list: # visual_obs is a dict containing observation for a single # time step (of course across a batch however) # visual_obs[<camera_name>]['rgb_tensor'] has shape [B, 3, H, W] # probably need to cast input to cuda # [B, -1, 3] keypoints = visual_obs[camera_name][ 'descriptor_keypoints_3d_world_frame'] # [B, K, 3] where K = len(spatial_descriptors_idx) keypoints = keypoints[:, spatial_descriptors_idx] B, K, _ = keypoints.shape # [B, K*3] keypoints_reshape = keypoints.reshape([B, K * 3]) if DEBUG: print("keypoints.shape", keypoints.shape) print("keypoints_reshape.shape", keypoints_reshape.shape) visual_model_output_list.append(keypoints_reshape) visual_model_output = None if len(visual_model_output_list) > 0: # concatenate this into a tensor # [B, n_samples, vision_model_out_dim] visual_model_output = torch.stack( visual_model_output_list, dim=1) else: visual_model_output = torch.Tensor( ) # empty tensor # states, actions = data assert actions.shape[1] == n_samples # cast this to float so it can be concatenated below visual_model_output = visual_model_output.type_as( observations) # we don't have any visual observations, so states are observations # states is gotten by concatenating visual_observations and observations # [B, n_samples, vision_model_out_dim + obs_dim] states = torch.cat((visual_model_output, observations), dim=-1) # state_cur: B x n_his x state_dim # state_cur = states[:, :n_his] # [B, n_his, state_dim] state_init = states[:, :n_his] # We want to rollout n_roll steps # actions = [B, n_his + n_roll, -1] # so we want action_seq.shape = [B, n_roll, -1] action_start_idx = 0 action_end_idx = n_his + n_roll - 1 action_seq = actions[:, action_start_idx: action_end_idx, :] if DEBUG: print("states.shape", states.shape) print("state_init.shape", state_init.shape) print("actions.shape", actions.shape) print("action_seq.shape", action_seq.shape) # try using models_dy.rollout_model instead of doing this manually rollout_data = rollout_model(state_init=state_init, action_seq=action_seq, dynamics_net=model_dy, compute_debug_data=False) # [B, n_roll, state_dim] state_rollout_pred = rollout_data['state_pred'] # [B, n_roll, state_dim] state_rollout_gt = states[:, n_his:] if DEBUG: print("state_rollout_gt.shape", state_rollout_gt.shape) print("state_rollout_pred.shape", state_rollout_pred.shape) # the loss function is between # [B, n_roll, state_dim] state_pred_err = state_rollout_pred - state_rollout_gt # [B, n_roll, object_state_dim] object_state_err = state_pred_err[:, :, object_state_indices] B, n_roll, object_state_dim = object_state_err.shape # [B, n_roll, *object_state_shape] object_state_err_reshape = object_state_err.reshape( [B, n_roll, *object_state_shape]) # num weights J = object_state_err_reshape.shape[2] weights = model_dy.weight_matrix assert len( weights) == J, "len(weights) = %d, but J = %d" % ( len(weights), J) # loss mse object, note the use of broadcasting semantics # [B, n_roll] object_state_loss_mse = weights * torch.pow( object_state_err_reshape, 2).sum(dim=-1) object_state_loss_mse = object_state_loss_mse.mean() l2_object = (weights * torch.norm( object_state_err_reshape, dim=-1)).mean() l2_object_final_step = (weights * torch.norm( object_state_err_reshape[:, -1], dim=-1)).mean() # [B, n_roll, robot_state_dim] robot_state_err = state_pred_err[:, :, robot_state_indices] robot_state_loss_mse = torch.pow(robot_state_err, 2).sum(dim=-1).mean() loss_container[ 'object_state_loss_mse'] = object_state_loss_mse loss_container[ 'robot_state_loss_mse'] = robot_state_loss_mse loss_container['l2_object'] = l2_object loss_container[ 'l2_object_final_step'] = l2_object_final_step # total loss loss = object_state_loss_mse + robot_state_loss_mse loss_container['loss'] = loss for key, val in loss_container.items(): if not key in average_meter_container: average_meter_container[key] = AverageMeter() average_meter_container[key].update(val.item(), B) step_duration_meter.update(time.time() - step_start_time) if phase == 'train': optimizer.zero_grad() loss.backward() optimizer.step() if (i % config['train']['log_per_iter'] == 0) or (global_iteration % config['train']['log_per_iter'] == 0): log = '%s [%d/%d][%d/%d] LR: %.6f' % ( phase, epoch, config['train']['n_epoch'], i, data_n_batches[phase], get_lr(optimizer)) # log += ', l2: %.6f' % (loss_container['l2'].item()) # log += ', l2_final_step: %.6f' %(loss_container['l2_final_step'].item()) log += ', step time %.6f' % (step_duration_meter.avg) step_duration_meter.reset() print(log) # log data to tensorboard # only do it once we have reached 100 iterations if global_iteration > 100: writer.add_scalar("Params/learning rate", get_lr(optimizer), global_iteration) writer.add_scalar("Loss_train/%s" % (phase), loss.item(), global_iteration) for loss_type, loss_obj in loss_container.items(): plot_name = "Loss/%s/%s" % (loss_type, phase) writer.add_scalar(plot_name, loss_obj.item(), counters[phase]) # only plot the weights if we are in the train phase . . . . if phase == "train": for i in range(len(weights)): plot_name = "Weights/%d" % (i) writer.add_scalar(plot_name, weights[i].item(), counters[phase]) if phase == 'train' and global_iteration % config['train'][ 'ckp_per_iter'] == 0: save_model( model_dy, '%s/net_dy_epoch_%d_iter_%d' % (train_dir, epoch, i)) log = '%s [%d/%d] Loss: %.6f, Best valid: %.6f' % ( phase, epoch, config['train']['n_epoch'], average_meter_container[valid_loss_type].avg, best_valid_loss) print(log) # record all average_meter losses for key, meter in average_meter_container.items(): writer.add_scalar("AvgMeter/%s/%s" % (key, phase), meter.avg, epoch) if phase == "train": if (scheduler is not None) and ( config['train']['lr_scheduler']['type'] == "StepLR"): scheduler.step() if phase == 'valid': if (scheduler is not None) and ( config['train']['lr_scheduler']['type'] == "ReduceLROnPlateau"): scheduler.step( average_meter_container[valid_loss_type].avg) if average_meter_container[ valid_loss_type].avg < best_valid_loss: best_valid_loss = average_meter_container[ valid_loss_type].avg training_stats['epoch'] = epoch training_stats['global_iteration'] = counters['valid'] save_yaml(training_stats, training_stats_file) save_model(model_dy, '%s/net_best_dy' % (train_dir)) writer.flush() # flush SummaryWriter events to disk except KeyboardInterrupt: # save network if we have a keyboard interrupt save_model( model_dy, '%s/net_dy_epoch_%d_keyboard_interrupt' % (train_dir, epoch_counter_external)) writer.flush() # flush SummaryWriter events to disk
from key_dynam.dataset.vision_function_factory import VisualObservationFunctionFactory from key_dynam.dataset.online_episode_reader import OnlineEpisodeReader from key_dynam.dataset.mpc_dataset import DynamicsModelInputBuilder from key_dynam.utils import meshcat_utils from key_dynam.planner.planners import RandomShootingPlanner, PlannerMPPI from key_dynam.utils import transform_utils, torch_utils from key_dynam.experiments.exp_09 import utils as exp_utils from key_dynam.utils import drake_image_utils from key_dynam.dynamics.utils import keypoints_3D_from_dynamics_model_output, set_seed from key_dynam.eval.utils import compute_pose_error from key_dynam.models import model_builder from key_dynam.models.model_builder import build_dynamics_model from key_dynam.autoencoder.autoencoder_models import ConvolutionalAutoencoder # constants set_seed(0) USE_FIXED_MPC_HORIZON = False USE_SHORT_HORIZON_MPC = False TERMINAL_COST_ONLY = True TRAJECTORY_GOAL = False REPLAN = True SEED_WITH_NOMINAL_ACTIONS = False N = 2 * 10 # horizon length for ground truth push N = 10 MPC_HORIZON = 10 PUSHER_VELOCITY = 0.20 PUSHER_ANGLE = np.deg2rad(20)
def main(): # load dynamics model model_dict = load_autoencoder_model() model = model_dict['model_dy'] model_ae = model_dict['model_ae'] visual_observation_function = model_dict['visual_observation_function'] config = model.config env_config = load_yaml( os.path.join(get_project_root(), 'experiments/exp_18_box_on_side/config.yaml')) env_config['env']['observation']['depth_int16'] = True n_history = config['train']['n_history'] # create the environment # create the environment env = DrakePusherSliderEnv(env_config) env.reset() # create another environment for doing rollouts env2 = DrakePusherSliderEnv(env_config, visualize=False) env2.reset() action_function = ActionFunctionFactory.function_from_config(config) observation_function = ObservationFunctionFactory.drake_pusher_position_3D( config) 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() initial_cond = get_initial_state() reset_environment(env, initial_cond['q_pusher'], initial_cond['q_slider']) obs_init = env.get_observation() # visualize starting position of the object print("obs_init.keys()", obs_init.keys()) print("obs_init['slider']['position']", obs_init['slider']['position']) T = DrakePusherSliderEnv.object_position_from_observation(obs_init) vis['start_pose'].set_object(triad(scale=0.1)) vis['state_pose'].set_transform(T) #### 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) #### ROLLOUT THE ACTION SEQUENCE USING THE SIMULATOR ########## # rollout single action sequence using the simulator gt_rollout_data = env_utils.rollout_action_sequence( env, initial_cond['action_sequence'].cpu().numpy()) env_obs_rollout_gt = gt_rollout_data['observations'] gt_rollout_episode = gt_rollout_data['episode_reader'] for i, env_obs in enumerate(gt_rollout_data['observations']): T = DrakePusherSliderEnv.object_position_from_observation(env_obs) vis_name = "GT_trajectory/%d" % (i) vis[vis_name].set_object(triad(scale=0.1)) vis[vis_name].set_transform(T) action_state_gt = mpc_input_builder.get_action_state_tensors( start_idx=0, num_timesteps=N, episode=gt_rollout_episode) state_rollout_gt = action_state_gt['states'] action_rollout_gt = action_state_gt['actions'] z_object_rollout_gt = model.compute_z_state( state_rollout_gt)['z_object_flat'] print('state_rollout_gt.shape', state_rollout_gt.shape) print("z_object_rollout_gt.shape", z_object_rollout_gt.shape) 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() # using the vision model to get "goal" keypoints z_object_goal = goal_func(env_obs_rollout_gt[-1]) z_object_goal_np = torch_utils.cast_to_numpy(z_object_goal) # 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) print("n_history", n_history) action_seq_gt_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'].squeeze(0) print("state_pred_rollout.shape", state_pred_rollout.shape) # input("press Enter to continue") # check L2 distance between predicted and actual # basically comparing state_pred_rollout and state_rollout_gt print("state_rollout_gt[-1]\n", state_rollout_gt[-1]) print("state_pred_rollout[-1]\n", state_pred_rollout[-1]) 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_config['mpc']['mppi']['terminal_cost_only'] = TERMINAL_COST_ONLY planner_config['mpc']['random_shooting'][ 'terminal_cost_only'] = TERMINAL_COST_ONLY 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 elif USE_SHORT_HORIZON_MPC: n_look_ahead = min(MPC_HORIZON, N - counter) if n_look_ahead == 0: break start_idx = counter end_idx = counter + n_look_ahead print("start_idx:", start_idx) print("end_idx:", end_idx) print("n_look_ahead", n_look_ahead) # 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 SEED_WITH_NOMINAL_ACTIONS: action_seq_rollout_init = action_seq_gt_torch[ start_idx:end_idx] else: if mpc_out is not None: action_seq_rollout_init = mpc_out['action_seq'][1:] print("action_seq_rollout_init.shape", action_seq_rollout_init.shape) if action_seq_rollout_init.shape[0] < n_look_ahead: num_steps = n_look_ahead - action_seq_rollout_init.shape[ 0] action_seq_zero = torch.zeros([num_steps, 2]) action_seq_rollout_init = torch.cat( (action_seq_rollout_init, action_seq_zero), dim=0) print("action_seq_rollout_init.shape", action_seq_rollout_init.shape) 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) if action_seq_rollout_init is not None: n_look_ahead = action_seq_rollout_init.shape[0] obs_goal = None print("z_object_rollout_gt.shape", z_object_rollout_gt.shape) if TRAJECTORY_GOAL: obs_goal = z_object_rollout_gt[start_idx:end_idx] print("n_look_ahead", n_look_ahead) print("obs_goal.shape", obs_goal.shape) # add the final goal state on as needed if obs_goal.shape[0] < n_look_ahead: obs_goal_final = z_object_rollout_gt[-1].unsqueeze(0) num_repeat = n_look_ahead - obs_goal.shape[0] obs_goal_final_expand = obs_goal_final.expand( [num_repeat, -1]) obs_goal = torch.cat((obs_goal, obs_goal_final_expand), dim=0) else: obs_goal = z_object_rollout_gt[-1] obs_goal = torch_utils.cast_to_numpy(obs_goal) print("obs_goal.shape", obs_goal.shape) seed = 1 set_seed(seed) mpc_out = planner.trajectory_optimization( state_cur=z_cur, action_his=action_his, obs_goal=obs_goal, 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, add_grid_action_samples=True, ) print("MPC step took %.4f seconds" % (time.time() - mpc_start_time)) action_seq_mpc = torch_utils.cast_to_numpy(mpc_out['action_seq']) state_pred_mpc = torch_utils.cast_to_numpy(mpc_out['state_pred']) # Rollout with ground truth simulator dynamics 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'] 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): # ground truth rollout of the MPC action_seq name = "mpc_GT_3D/%d" % (i) T_W_obj = DrakePusherSliderEnv.object_position_from_observation( obs_mpc_gt[i]) vis[name].set_object(triad(scale=0.1)) vis[name].set_transform(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) # update the trajectories, in case we aren't replanning action_seq_mpc = action_seq_mpc[1:] state_pred_mpc = state_pred_mpc[1:] pose_error = compute_pose_error(env_obs_rollout_gt[-1], obs_cur) print("position_error: %.3f" % (pose_error['position_error'])) print("angle error degrees: %.3f" % (pose_error['angle_error_degrees'])) obs_final = env.get_observation() pose_error = compute_pose_error(env_obs_rollout_gt[-1], obs_final) print("position_error: %.3f" % (pose_error['position_error'])) print("angle error degrees: %.3f" % (pose_error['angle_error_degrees']))
import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from progressbar import ProgressBar from torch.optim.lr_scheduler import ReduceLROnPlateau from torch.utils.data import Dataset, DataLoader import torchvision.transforms as transforms from torch.distributions.multivariate_normal import MultivariateNormal from key_dynam.dynamics.config import gen_args from key_dynam.dynamics.data import PhysicsDataset, load_data from key_dynam.dynamics.models_dy import DynaNetGNN from key_dynam.dynamics.utils import rand_int, count_trainable_parameters, Tee, AverageMeter, get_lr, to_np, set_seed args = gen_args() set_seed(args.random_seed) torch.manual_seed(args.random_seed) np.random.seed(args.random_seed) os.system('mkdir -p ' + args.dataf) os.system('mkdir -p ' + args.outf_dy) tee = Tee(os.path.join(args.outf_dy, 'train.log'), 'w') print(args) # generate data trans_to_tensor = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) ])
# GLOBAL_TRANSLATION = np.array([-0.1, 0.0, 0]) # doesn't work with any offset GLOBAL_TRANSLATION = np.array([0,0,0]) SEED_MPC_W_GT_ACTION_SEQUENCE = False RANDOM_SEED = 3 OBJECT_YAW = np.deg2rad(-90) REPLAN = True PLANNER_TYPE = "mppi" # PLANNER_TYPE = "random_shooting" BOX_ON_SIDE = True set_seed(RANDOM_SEED) def sample_object_position(T_aug=None, upright=False): pos = np.array([0, 0, 0.1]) quat = None if upright: quat = np.array([1,0,0,0]) else: quat = transforms3d.euler.euler2quat(np.deg2rad(90), 0, 0) T_O_slider = transform_utils.transform_from_pose(pos, quat) # apply a random yaw to the object yaw = OBJECT_YAW
def run_precompute_descriptors_pipeline(multi_episode_dict, # dict model, # dense descriptor model file model_file=None, output_dir=None, # str where to save data episode_name=None, # optional for descriptor sampling camera_name=None, # which camera to compute descriptors for episode_idx=None, # optional for descriptor sampling visualize=True, K=5, position_diff_threshold=20, seed=0, ): assert model_file is not None assert camera_name is not None # # ## Load Model # model_train_dir = os.path.dirname(model_file) # # print("model_train_dir", model_train_dir) # print("model_file", model_file) # model = torch.load(model_file) # model = model.cuda() # model = model.eval() # # output_dir = os.path.join(model_train_dir, # 'precomputed_vision_data/descriptor_keypoints/dataset_%s/' % (dataset_name)) # compute descriptor confidence scores set_seed(seed) if True: print("\n\n---------Computing Descriptor Confidence Scores-----------") metadata_file = os.path.join(output_dir, 'metadata.p') if os.path.isfile(metadata_file): answer = input("metadata.p file already exists, do you want to overwrite it? y/n\n") if answer == "y": shutil.rmtree(output_dir) print("removing existing file and continuing") else: print("aborting") quit() compute_descriptor_confidences(multi_episode_dict, model, output_dir, batch_size=10, num_workers=20, model_file=model_file, camera_name=camera_name, num_ref_descriptors=50, num_batches=10, episode_name_arg=episode_name, episode_idx=episode_idx, ) if True: confidence_score_data_file = os.path.join(output_dir, 'data.p') confidence_score_data = load_pickle(confidence_score_data_file) metadata_file = os.path.join(output_dir, 'metadata.p') metadata = load_pickle(metadata_file) print("\n\n---------Selecting Spatially Separated Keypoints-----------") score_and_select_spatially_separated_keypoints(metadata, confidence_score_data=confidence_score_data, K=K, position_diff_threshold=position_diff_threshold, output_dir=output_dir, ) # visualize descriptors if True: metadata_file = os.path.join(output_dir, 'metadata.p') metadata = load_pickle(metadata_file) episode_name = metadata['episode_name'] episode_idx = metadata['episode_idx'] camera_name = metadata['camera_name'] episode = multi_episode_dict[episode_name] data = episode.get_image_data(camera_name, episode_idx) rgb = data['rgb'] uv = metadata['indices'] print("uv.shape", uv.shape) color = [0, 255, 0] draw_reticles(rgb, uv[:, 0], uv[:, 1], label_color=color) save_file = os.path.join(output_dir, 'sampled_descriptors.png') plt.figure() plt.imshow(rgb) plt.savefig(save_file) if visualize: plt.show() # visualize spatially separated descriptors if True: metadata_file = os.path.join(output_dir, 'metadata.p') metadata = load_pickle(metadata_file) spatial_descriptor_file = os.path.join(output_dir, 'spatial_descriptors.p') spatial_descriptors_data = load_pickle(spatial_descriptor_file) des_idx = spatial_descriptors_data['spatial_descriptors_idx'] episode_name = metadata['episode_name'] episode_idx = metadata['episode_idx'] camera_name = metadata['camera_name'] episode = multi_episode_dict[episode_name] data = episode.get_image_data(camera_name, episode_idx) rgb = data['rgb'] uv = metadata['indices'] print("uv.shape", uv.shape) color = [0, 255, 0] draw_reticles(rgb, uv[des_idx, 0], uv[des_idx, 1], label_color=color) save_file = os.path.join(output_dir, 'spatially_separated_descriptors.png') plt.figure() plt.imshow(rgb) plt.savefig(save_file) if visualize: plt.show() if True: metadata_file = os.path.join(output_dir, 'metadata.p') metadata = load_pickle(metadata_file) print("\n\n---------Precomputing Descriptor Keypoints-----------") descriptor_keypoints_output_dir = os.path.join(output_dir, "descriptor_keypoints") precompute_descriptor_keypoints(multi_episode_dict, model, descriptor_keypoints_output_dir, ref_descriptors_metadata=metadata, batch_size=8, num_workers=20, camera_names=[camera_name] ) print("Data saved at: ", output_dir) print("Finished Normally")