systems/babyai-sr/code/helper.py

from argparse import Namespace
import json
from pathlib import Path
from typing import Tuple, List, Dict

import gym
import torch
import numpy as np

import babyai.utils as utils
import babyai_sr.utils as utils_sr
from babyai_sr.arguments import ArgumentParser
from babyai_sr.rl.algos import TestAlgo, BaseAlgo
from babyai_sr.rl.utils import ParallelEnv
import util as lib_util

##### based on repo/babyai_sr/rl/algos/test.py #####

SENDER = 0
RECEIVER = 1


def extract_messages(message, done, active) -> List[List[int]]:
    argmax_messages = message.argmax(-1)[:, :, SENDER]
    done_np = done.cpu().numpy()
    done_idxs = np.argwhere(done_np)
    message_list = []
    for i in range(done_idxs.shape[0]):
        prev_end = done_idxs[i - 1] if i > 0 else [0, -1]
        end = done_idxs[i]
        # If we are spanning the border between procs, adjust the prev_end
        # to start at the end's proc.
        if prev_end[0] != end[0]:
            prev_end = [end[0], -1]
        # Take messages from the given proc and
        indexer = np.s_[prev_end[0], prev_end[1] + 1 : (end[1] + 1)]
        ep_messages = argmax_messages[indexer]
        # We only want messages for when the sender is active since the
        # message is just repeated for the receiver until the sender is
        # active again.
        ep_active = active[indexer][:, SENDER]
        final_ep_messages = ep_messages[ep_active]
        message_list.append(final_ep_messages.cpu().tolist())
    return message_list


class TestAlgo(BaseAlgo):
    def __init__(
        self,
        env,
        models,
        num_frames_per_proc=40,
        discount=0.99,
        gae_lambda=0.99,
        preprocess_obss=None,
        reshape_reward=None,
        use_comm=True,
        conventional=False,
        argmax=True,
    ):

        super().__init__(
            env,
            models,
            num_frames_per_proc,
            discount,
            gae_lambda,
            preprocess_obss,
            reshape_reward,
            use_comm,
            conventional,
            argmax,
        )

    def collect_episodes(self, episodes):
        # Collect experiences.
        exps, _ = self.collect_experiences()
        batch = 1

        shape_prefix = (self.num_procs, self.num_frames_per_proc)

        active = exps.active.view(*shape_prefix, *exps.active.shape[1:])
        done = exps.done.view(*shape_prefix, *exps.done.shape[1:])
        message = exps.message.view(*shape_prefix, *exps.message.shape[1:])
        reward = exps.reward.view(*shape_prefix, *exps.reward.shape[1:])

        log = {
            "return_per_episode": [],
        }

        proc = 0
        frame = [0] * self.num_procs
        episodes_done = 0
        while True:
            if done[proc, frame[proc]]:
                episodes_done += 1
                log["return_per_episode"].append(
                    reward[proc, frame[proc], RECEIVER].item()
                )
                if episodes_done == episodes:
                    break
                frame[proc] += 1
                proc = (proc + 1) % self.num_procs
            else:
                frame[proc] += 1

            if frame[proc] == batch * self.num_frames_per_proc:
                exps, _ = self.collect_experiences()
                batch += 1

                next_active = exps.active.view(*shape_prefix, *exps.active.shape[1:])
                next_done = exps.done.view(*shape_prefix, *exps.done.shape[1:])
                next_message = exps.message.view(*shape_prefix, *exps.message.shape[1:])
                next_reward = exps.reward.view(*shape_prefix, *exps.reward.shape[1:])

                active = torch.cat((active, next_active), 1)
                done = torch.cat((done, next_done), 1)
                message = torch.cat((message, next_message), 1)
                reward = torch.cat((reward, next_reward), 1)

        message_list = extract_messages(message, done, active)

        return message_list, log


##### based on repo/babyai_sr/scripts/test_rl.py #####


def parse_args() -> Namespace:
    parser = ArgumentParser()
    parser.add_argument("--name", help="name of the configuration")
    parser.add_argument("--sender", default=None, help="name of the sender (REQUIRED)")
    parser.add_argument(
        "--receiver", default=None, help="name of the receiver (REQUIRED)"
    )
    parser.add_argument(
        "--sample",
        action="store_true",
        default=False,
        help="sample messages instead of using argmax",
    )
    parser.add_argument(
        "--episodes",
        type=int,
        default=1000,
        help="number of episodes to test on (default: 1000)",
    )
    parser.add_argument("--len-message", default=None, help="dummy")
    parser.add_argument("--num-symbols", default=None, help="dummy")
    return parser.parse_args()


def get_episodes(args: Namespace) -> Tuple[List, Dict]:
    utils.seed(args.seed)
    envs = []
    for i in range(args.procs):
        env = gym.make(args.env)
        env.seed(100 * args.seed + i)
        envs.append(env)
    penv = ParallelEnv(
        envs, args.n, args.conventional, args.archimedean, args.informed_sender
    )

    sender = utils.load_model(args.sender)
    receiver = utils.load_model(args.receiver)
    sender.eval()
    receiver.eval()
    if torch.cuda.is_available():
        sender.cuda()
        receiver.cuda()

    reshape_reward = lambda _0, _1, reward, _2: args.reward_scale * reward
    obss_preprocessor = utils_sr.MultiObssPreprocessor(
        [args.sender, args.receiver], [envs[0].observation_space] * 2
    )
    test_algo = TestAlgo(
        penv,
        [sender, receiver],
        args.frames_per_proc,
        args.discount,
        args.gae_lambda,
        obss_preprocessor,
        reshape_reward,
        not args.no_comm,
        args.conventional,
        not args.sample,
    )

    return test_algo.collect_episodes(args.episodes)


def write_results(args: Namespace, message_list: List, logs: Dict) -> None:
    out_path = Path(f"../../data/{args.name}")
    out_path.mkdir(exist_ok=True, parents=True)

    return_per_episode = utils.synthesize(logs["return_per_episode"])
    success_per_episode = utils.synthesize(
        [1 if r > 0 else 0 for r in logs["return_per_episode"]]
    )

    metrics = {
        "success_rate": success_per_episode["mean"],
        "success_rate_std": success_per_episode["std"],
        "reward_per_episode": return_per_episode["mean"],
        "reward_per_episode_std": return_per_episode["std"],
    }
    lib_util.write_system_metrics(out_path, metrics)

    with (out_path / "corpus.jsonl").open("w") as fo:
        for message in message_list:
            flat = [x for y in message for x in y]
            json.dump(flat, fo, indent=None)
            fo.write("\n")
    with (out_path / "corpus.structured.jsonl").open("w") as fo:
        for message in message_list:
            json.dump(message, fo, indent=None)
            fo.write("\n")


def main() -> None:
    args = parse_args()
    message_list, logs = get_episodes(args)
    write_results(args, message_list, logs)


if __name__ == "__main__":
    main()