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