src/evaluation.py

import fnmatch
import os
from typing import Dict, SupportsFloat

import gymnasium as gym
import numpy as np
import torch
from gymnasium import wrappers
from huggingface_hub import HfApi
from huggingface_hub.utils._errors import EntryNotFoundError

from src.logging import setup_logger

logger = setup_logger(__name__)

API = HfApi(token=os.environ.get("TOKEN"))

logger.info(f"Is CUDA available: {torch.cuda.is_available()}")
logger.info(f"CUDA device: {torch.cuda.get_device_name(torch.cuda.current_device())}")


ALL_ENV_IDS = [
    "AdventureNoFrameskip-v4",
    "AirRaidNoFrameskip-v4",
    "AlienNoFrameskip-v4",
    "AmidarNoFrameskip-v4",
    "AssaultNoFrameskip-v4",
    "AsterixNoFrameskip-v4",
    "AsteroidsNoFrameskip-v4",
    "AtlantisNoFrameskip-v4",
    "BankHeistNoFrameskip-v4",
    "BattleZoneNoFrameskip-v4",
    "BeamRiderNoFrameskip-v4",
    "BerzerkNoFrameskip-v4",
    "BowlingNoFrameskip-v4",
    "BoxingNoFrameskip-v4",
    "BreakoutNoFrameskip-v4",
    "CarnivalNoFrameskip-v4",
    "CentipedeNoFrameskip-v4",
    "ChopperCommandNoFrameskip-v4",
    "CrazyClimberNoFrameskip-v4",
    "DefenderNoFrameskip-v4",
    "DemonAttackNoFrameskip-v4",
    "DoubleDunkNoFrameskip-v4",
    "ElevatorActionNoFrameskip-v4",
    "EnduroNoFrameskip-v4",
    "FishingDerbyNoFrameskip-v4",
    "FreewayNoFrameskip-v4",
    "FrostbiteNoFrameskip-v4",
    "GopherNoFrameskip-v4",
    "GravitarNoFrameskip-v4",
    "HeroNoFrameskip-v4",
    "IceHockeyNoFrameskip-v4",
    "JamesbondNoFrameskip-v4",
    "JourneyEscapeNoFrameskip-v4",
    "KangarooNoFrameskip-v4",
    "KrullNoFrameskip-v4",
    "KungFuMasterNoFrameskip-v4",
    "MontezumaRevengeNoFrameskip-v4",
    "MsPacmanNoFrameskip-v4",
    "NameThisGameNoFrameskip-v4",
    "PhoenixNoFrameskip-v4",
    "PitfallNoFrameskip-v4",
    "PongNoFrameskip-v4",
    "PooyanNoFrameskip-v4",
    "PrivateEyeNoFrameskip-v4",
    "QbertNoFrameskip-v4",
    "RiverraidNoFrameskip-v4",
    "RoadRunnerNoFrameskip-v4",
    "RobotankNoFrameskip-v4",
    "SeaquestNoFrameskip-v4",
    "SkiingNoFrameskip-v4",
    "SolarisNoFrameskip-v4",
    "SpaceInvadersNoFrameskip-v4",
    "StarGunnerNoFrameskip-v4",
    "TennisNoFrameskip-v4",
    "TimePilotNoFrameskip-v4",
    "TutankhamNoFrameskip-v4",
    "UpNDownNoFrameskip-v4",
    "VentureNoFrameskip-v4",
    "VideoPinballNoFrameskip-v4",
    "WizardOfWorNoFrameskip-v4",
    "YarsRevengeNoFrameskip-v4",
    "ZaxxonNoFrameskip-v4",
    # Box2D
    "BipedalWalker-v3",
    "BipedalWalkerHardcore-v3",
    "CarRacing-v2",
    "LunarLander-v2",
    "LunarLanderContinuous-v2",
    # Toy text
    "Blackjack-v1",
    "CliffWalking-v0",
    "FrozenLake-v1",
    "FrozenLake8x8-v1",
    # Classic control
    "Acrobot-v1",
    "CartPole-v1",
    "MountainCar-v0",
    "MountainCarContinuous-v0",
    "Pendulum-v1",
    # MuJoCo
    "Ant-v4",
    "HalfCheetah-v4",
    "Hopper-v4",
    "Humanoid-v4",
    "HumanoidStandup-v4",
    "InvertedDoublePendulum-v4",
    "InvertedPendulum-v4",
    "Pusher-v4",
    "Reacher-v4",
    "Swimmer-v4",
    "Walker2d-v4",
]


class NoopResetEnv(gym.Wrapper[np.ndarray, int, np.ndarray, int]):
    """
    Sample initial states by taking random number of no-ops on reset.
    No-op is assumed to be action 0.

    :param env: Environment to wrap
    :param noop_max: Maximum value of no-ops to run
    """

    def __init__(self, env: gym.Env, noop_max: int = 30) -> None:
        super().__init__(env)
        self.noop_max = noop_max
        self.override_num_noops = None
        self.noop_action = 0
        assert env.unwrapped.get_action_meanings()[0] == "NOOP"  # type: ignore[attr-defined]

    def reset(self, **kwargs):
        self.env.reset(**kwargs)
        if self.override_num_noops is not None:
            noops = self.override_num_noops
        else:
            noops = self.unwrapped.np_random.integers(1, self.noop_max + 1)
        assert noops > 0
        obs = np.zeros(0)
        info: Dict = {}
        for _ in range(noops):
            obs, _, terminated, truncated, info = self.env.step(self.noop_action)
            if terminated or truncated:
                obs, info = self.env.reset(**kwargs)
        return obs, info


class FireResetEnv(gym.Wrapper[np.ndarray, int, np.ndarray, int]):
    """
    Take action on reset for environments that are fixed until firing.

    :param env: Environment to wrap
    """

    def __init__(self, env: gym.Env) -> None:
        super().__init__(env)
        assert env.unwrapped.get_action_meanings()[1] == "FIRE"  # type: ignore[attr-defined]
        assert len(env.unwrapped.get_action_meanings()) >= 3  # type: ignore[attr-defined]

    def reset(self, **kwargs):
        self.env.reset(**kwargs)
        obs, _, terminated, truncated, _ = self.env.step(1)
        if terminated or truncated:
            self.env.reset(**kwargs)
        obs, _, terminated, truncated, _ = self.env.step(2)
        if terminated or truncated:
            self.env.reset(**kwargs)
        return obs, {}


class EpisodicLifeEnv(gym.Wrapper[np.ndarray, int, np.ndarray, int]):
    """
    Make end-of-life == end-of-episode, but only reset on true game over.
    Done by DeepMind for the DQN and co. since it helps value estimation.

    :param env: Environment to wrap
    """

    def __init__(self, env: gym.Env) -> None:
        super().__init__(env)
        self.lives = 0
        self.was_real_done = True

    def step(self, action: int):
        obs, reward, terminated, truncated, info = self.env.step(action)
        self.was_real_done = terminated or truncated
        # check current lives, make loss of life terminal,
        # then update lives to handle bonus lives
        lives = self.env.unwrapped.ale.lives()  # type: ignore[attr-defined]
        if 0 < lives < self.lives:
            # for Qbert sometimes we stay in lives == 0 condition for a few frames
            # so its important to keep lives > 0, so that we only reset once
            # the environment advertises done.
            terminated = True
        self.lives = lives
        return obs, reward, terminated, truncated, info

    def reset(self, **kwargs):
        """
        Calls the Gym environment reset, only when lives are exhausted.
        This way all states are still reachable even though lives are episodic,
        and the learner need not know about any of this behind-the-scenes.

        :param kwargs: Extra keywords passed to env.reset() call
        :return: the first observation of the environment
        """
        if self.was_real_done:
            obs, info = self.env.reset(**kwargs)
        else:
            # no-op step to advance from terminal/lost life state
            obs, _, terminated, truncated, info = self.env.step(0)

            # The no-op step can lead to a game over, so we need to check it again
            # to see if we should reset the environment and avoid the
            # monitor.py `RuntimeError: Tried to step environment that needs reset`
            if terminated or truncated:
                obs, info = self.env.reset(**kwargs)
        self.lives = self.env.unwrapped.ale.lives()  # type: ignore[attr-defined]
        return obs, info


class MaxAndSkipEnv(gym.Wrapper[np.ndarray, int, np.ndarray, int]):
    """
    Return only every ``skip``-th frame (frameskipping)
    and return the max between the two last frames.

    :param env: Environment to wrap
    :param skip: Number of ``skip``-th frame
        The same action will be taken ``skip`` times.
    """

    def __init__(self, env: gym.Env, skip: int = 4) -> None:
        super().__init__(env)
        # most recent raw observations (for max pooling across time steps)
        assert env.observation_space.dtype is not None, "No dtype specified for the observation space"
        assert env.observation_space.shape is not None, "No shape defined for the observation space"
        self._obs_buffer = np.zeros((2, *env.observation_space.shape), dtype=env.observation_space.dtype)
        self._skip = skip

    def step(self, action: int):
        """
        Step the environment with the given action
        Repeat action, sum reward, and max over last observations.

        :param action: the action
        :return: observation, reward, terminated, truncated, information
        """
        total_reward = 0.0
        terminated = truncated = False
        for i in range(self._skip):
            obs, reward, terminated, truncated, info = self.env.step(action)
            done = terminated or truncated
            if i == self._skip - 2:
                self._obs_buffer[0] = obs
            if i == self._skip - 1:
                self._obs_buffer[1] = obs
            total_reward += float(reward)
            if done:
                break
        # Note that the observation on the done=True frame
        # doesn't matter
        max_frame = self._obs_buffer.max(axis=0)

        return max_frame, total_reward, terminated, truncated, info


class ClipRewardEnv(gym.RewardWrapper):
    """
    Clip the reward to {+1, 0, -1} by its sign.

    :param env: Environment to wrap
    """

    def __init__(self, env: gym.Env) -> None:
        super().__init__(env)

    def reward(self, reward: SupportsFloat) -> float:
        """
        Bin reward to {+1, 0, -1} by its sign.

        :param reward:
        :return:
        """
        return np.sign(float(reward))


def make(env_id):
    def thunk():
        env = gym.make(env_id)
        env = wrappers.RecordEpisodeStatistics(env)
        if "NoFrameskip" in env_id:
            env = NoopResetEnv(env, noop_max=30)
            env = MaxAndSkipEnv(env, skip=4)
            env = EpisodicLifeEnv(env)
            if "FIRE" in env.unwrapped.get_action_meanings():
                env = FireResetEnv(env)
            env = ClipRewardEnv(env)
            env = wrappers.ResizeObservation(env, (84, 84))
            env = wrappers.GrayScaleObservation(env)
            env = wrappers.FrameStack(env, 4)
        return env

    return thunk


def pattern_match(patterns, source_list):
    if isinstance(patterns, str):
        patterns = [patterns]

    env_ids = set()
    for pattern in patterns:
        for matching in fnmatch.filter(source_list, pattern):
            env_ids.add(matching)
    return sorted(list(env_ids))


def evaluate(model_id, revision):
    tags = API.model_info(model_id, revision=revision).tags

    # Extract the environment IDs from the tags (usually only one)
    env_ids = pattern_match(tags, ALL_ENV_IDS)
    logger.info(f"Selected environments: {env_ids}")

    results = {}

    # Check if the agent exists
    try:
        agent_path = API.hf_hub_download(repo_id=model_id, filename="agent.pt")
    except EntryNotFoundError:
        logger.error("Agent not found")
        return None

    # Check safety
    security = next(iter(API.get_paths_info(model_id, "agent.pt", expand=True))).security
    if security is None or "safe" not in security:
        logger.error("Agent safety not available")
        return None
    elif not security["safe"]:
        logger.error("Agent not safe")
        return None

    # Load the agent
    try:
        agent = torch.jit.load(agent_path).to("cuda")
    except Exception as e:
        logger.error(f"Error loading agent: {e}")
        return None

    # Evaluate the agent on the environments
    for env_id in env_ids:
        envs = gym.vector.SyncVectorEnv([make(env_id) for _ in range(3)])
        observations, _ = envs.reset()
        episodic_returns = []
        while len(episodic_returns) < 10:
            actions = agent(torch.tensor(observations, device="cuda")).cpu().numpy()
            observations, _, _, _, infos = envs.step(actions)
            if "final_info" in infos:
                for info in infos["final_info"]:
                    if info is None or "episode" not in info:
                        continue
                    episodic_returns.append(float(info["episode"]["r"]))

        results[env_id] = {"episodic_returns": episodic_returns}
        logger.info(f"Environment {env_id}: {np.mean(episodic_returns)} ± {np.std(episodic_returns)}")
    return results