03_eval_finetuned.py

"""
This script demonstrates how to load and rollout a finetuned Octo model.
We use the Octo model finetuned on ALOHA sim data from the examples/finetune_new_observation_action.py script.

For installing the ALOHA sim environment, clone: https://github.com/tonyzhaozh/act
Then run:
pip3 install opencv-python modern_robotics pyrealsense2 h5py_cache pyquaternion pyyaml rospkg pexpect mujoco==2.3.3 dm_control==1.0.9 einops packaging h5py

Finally, modify the `sys.path.append` statement below to add the ACT repo to your path.
If you are running this on a head-less server, start a virtual display:
    Xvfb :1 -screen 0 1024x768x16 &
    export DISPLAY=:1

To run this script, run:
    cd examples
    python3 03_eval_finetuned.py --finetuned_path=<path_to_finetuned_aloha_checkpoint>
"""
import sys

from absl import app, flags, logging
import gym
import jax
import numpy as np
import wandb

sys.path.append("path/to/your/act")

from envs.aloha_sim_env import AlohaGymEnv  # keep this to register ALOHA sim env

from octo.model.octo_model import OctoModel
from octo.utils.gym_wrappers import HistoryWrapper, RHCWrapper, UnnormalizeActionProprio

FLAGS = flags.FLAGS

flags.DEFINE_string(
    "finetuned_path", None, "Path to finetuned Octo checkpoint directory."
)


def main(_):
    # setup wandb for logging
    wandb.init(name="eval_aloha", project="octo")

    # load finetuned model
    logging.info("Loading finetuned model...")
    model = OctoModel.load_pretrained(FLAGS.finetuned_path)

    # make gym environment
    ##################################################################################################################
    # environment needs to implement standard gym interface + return observations of the following form:
    #   obs = {
    #     "image_0": ...
    #     "image_1": ...
    #   }
    # it should also implement an env.get_task() function that returns a task dict with goal and/or language instruct.
    #   task = {
    #     "language_instruction": "some string"
    #     "goal": {
    #       "image_0": ...
    #       "image_1": ...
    #     }
    #   }
    ##################################################################################################################
    env = gym.make("aloha-sim-cube-v0")

    # add wrappers for history and "receding horizon control", i.e. action chunking
    env = HistoryWrapper(env, horizon=1)
    env = RHCWrapper(env, exec_horizon=50)

    # wrap env to handle action/proprio normalization -- match normalization type to the one used during finetuning
    env = UnnormalizeActionProprio(
        env, model.dataset_statistics, normalization_type="normal"
    )

    # running rollouts
    for _ in range(3):
        obs, info = env.reset()

        # create task specification --> use model utility to create task dict with correct entries
        language_instruction = env.get_task()["language_instruction"]
        task = model.create_tasks(texts=language_instruction)

        # run rollout for 400 steps
        images = [obs["image_primary"][0]]
        episode_return = 0.0
        while len(images) < 400:
            # model returns actions of shape [batch, pred_horizon, action_dim] -- remove batch
            actions = model.sample_actions(
                jax.tree_map(lambda x: x[None], obs), task, rng=jax.random.PRNGKey(0)
            )
            actions = actions[0]

            # step env -- info contains full "chunk" of observations for logging
            # obs only contains observation for final step of chunk
            obs, reward, done, trunc, info = env.step(actions)
            images.extend([o["image_primary"][0] for o in info["observations"]])
            episode_return += reward
            if done or trunc:
                break
        print(f"Episode return: {episode_return}")

        # log rollout video to wandb -- subsample temporally 2x for faster logging
        wandb.log(
            {"rollout_video": wandb.Video(np.array(images).transpose(0, 3, 1, 2)[::2])}
        )


if __name__ == "__main__":
    app.run(main)