policy/ACT/deploy_policy.py

import sys
import numpy as np
import torch
import os
import pickle
import cv2
import time  # Add import for timestamp
import h5py  # Add import for HDF5
from datetime import datetime  # Add import for datetime formatting
from .act_policy import ACT
import copy
from argparse import Namespace

def encode_obs(observation):
    head_cam = cv2.resize(observation["observation"]["head_camera"]["rgb"], (640, 480), interpolation=cv2.INTER_LINEAR)
    left_cam = cv2.resize(observation["observation"]["left_camera"]["rgb"], (640, 480), interpolation=cv2.INTER_LINEAR)
    right_cam = cv2.resize(observation["observation"]["right_camera"]["rgb"], (640, 480), interpolation=cv2.INTER_LINEAR)
    head_cam = np.moveaxis(head_cam, -1, 0) / 255.0
    left_cam = np.moveaxis(left_cam, -1, 0) / 255.0
    right_cam = np.moveaxis(right_cam, -1, 0) / 255.0
    qpos = (observation["joint_action"]["left_arm"] + [observation["joint_action"]["left_gripper"]] +
            observation["joint_action"]["right_arm"] + [observation["joint_action"]["right_gripper"]])
    return {
        "head_cam": head_cam,
        "left_cam": left_cam,
        "right_cam": right_cam,
        "qpos": qpos,
    }

def get_model(usr_args):
    return ACT(usr_args, Namespace(**usr_args))


def eval(TASK_ENV, model, observation):
    obs = encode_obs(observation)
    # instruction = TASK_ENV.get_instruction()

    # Get action from model
    actions = model.get_action(obs)
    for action in actions:
        TASK_ENV.take_action(action)
        observation = TASK_ENV.get_obs()
    return observation


def reset_model(model):
    # Reset temporal aggregation state if enabled
    if model.temporal_agg:
        model.all_time_actions = torch.zeros([
            model.max_timesteps,
            model.max_timesteps + model.num_queries,
            model.state_dim,
        ]).to(model.device)
        model.t = 0
        print("Reset temporal aggregation state")
    else:
        model.t = 0