"""Utils for evaluating robot policies in various environments.""" import os import random import time import numpy as np import torch from openvla_utils import ( get_vla_action, get_vla_action_v2, ) # Initialize important constants and pretty-printing mode in NumPy. ACTION_DIM = 7 DATE = time.strftime("%Y_%m_%d") DATE_TIME = time.strftime("%Y_%m_%d-%H_%M_%S") DEVICE = torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu") np.set_printoptions(formatter={"float": lambda x: "{0:0.3f}".format(x)}) # Initialize system prompt for OpenVLA v0.1. OPENVLA_V01_SYSTEM_PROMPT = ( "A chat between a curious user and an artificial intelligence assistant. " "The assistant gives helpful, detailed, and polite answers to the user's questions." ) def set_seed_everywhere(seed: int): """Sets the random seed for Python, NumPy, and PyTorch functions.""" torch.manual_seed(seed) torch.cuda.manual_seed_all(seed) np.random.seed(seed) random.seed(seed) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False os.environ["PYTHONHASHSEED"] = str(seed) def get_image_resize_size(cfg): """ Gets image resize size for a model class. If `resize_size` is an int, then the resized image will be a square. Else, the image will be a rectangle. """ if cfg.model_family == "openvla" or cfg.model_family == "customvla" or cfg.model_family == 'objectvla': resize_size = 224 else: raise ValueError("Unexpected `model_family` found in config.") return resize_size def get_action(cfg, model, obs, task_label, processor=None): """Queries the model to get an action.""" if cfg.model_family == "openvla" or cfg.model_family == "customvla" or cfg.model_family == 'objectvla': action = get_vla_action( model, processor, cfg.pretrained_checkpoint, obs, task_label, cfg.unnorm_key, center_crop=cfg.center_crop ) assert action.shape == (ACTION_DIM,) else: raise ValueError("Unexpected `model_family` found in config.") return action def get_action_v2(cfg, model, obs, task_label, processor=None): """Queries the model to get an action.""" if cfg.model_family == "openvla" or cfg.model_family == "customvla" or cfg.model_family == 'objectvla': action = get_vla_action_v2( model, processor, cfg.pretrained_checkpoint, obs, task_label, cfg.unnorm_key, center_crop=cfg.center_crop ) assert action.shape == (ACTION_DIM,) else: raise ValueError("Unexpected `model_family` found in config.") return action def get_action_v3(cfg, model, obs, task_label, processor=None): """Queries the model to get an action.""" if cfg.model_family == "openvla" or cfg.model_family == "customvla" or cfg.model_family == 'objectvla': action = get_vla_action_v3( model, processor, cfg.pretrained_checkpoint, obs, task_label, cfg.unnorm_key, center_crop=cfg.center_crop ) assert action.shape == (ACTION_DIM,) else: raise ValueError("Unexpected `model_family` found in config.") return action def normalize_gripper_action(action, binarize=True): """ Changes gripper action (last dimension of action vector) from [0,1] to [-1,+1]. Necessary for some environments (not Bridge) because the dataset wrapper standardizes gripper actions to [0,1]. Note that unlike the other action dimensions, the gripper action is not normalized to [-1,+1] by default by the dataset wrapper. Normalization formula: y = 2 * (x - orig_low) / (orig_high - orig_low) - 1 """ # Just normalize the last action to [-1,+1]. orig_low, orig_high = 0.0, 1.0 action[..., -1] = 2 * (action[..., -1] - orig_low) / (orig_high - orig_low) - 1 if binarize: # Binarize to -1 or +1. action[..., -1] = np.sign(action[..., -1]) return action def invert_gripper_action(action): """ Flips the sign of the gripper action (last dimension of action vector). This is necessary for some environments where -1 = open, +1 = close, since the RLDS dataloader aligns gripper actions such that 0 = close, 1 = open. """ action[..., -1] = action[..., -1] * -1.0 return action