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"""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 |