How to use test_in_env method in lisa

Best Python code snippet using lisa_python

Experiments.py

Source:Experiments.py

...216        for e in range(self.max_batch_episodes):217            # Make a gradient update step218            self.learner.train(batch)219            for _ in range(10):220                partial_result = self.test_in_env()221                self.episode_returns.append(partial_result)222            if self.plot_frequency is not None and (e + 1) % self.plot_frequency == 0:223                self.plot_training(update=True)224                if self.print_when_plot:225                    print('Batch %u, epi-return %.4g +- %.3g' %226                          (len(self.episode_returns), np.mean(self.episode_returns[-10:]),227                           np.std(self.episode_returns[-10:])))228    def test_in_env(self):229        rewards = []230        for _ in range(10):231        # for _ in range(self.plot_frequency):232            state = self.runner.env.reset()233            done = False234            score = 0235            while not done:236                action = self.controller.choose(state, increase_counter=False).detach().item()237                # action = 1238                new_state, reward, done = self.runner.env.step(action)239                score += reward240                state = new_state241            rewards.append(score)242        return np.mean(score)243class BatchHeuristicActorCriticExperiment(Experiment):244    def __init__(self, params, model, learner=None, **kwargs):245        super().__init__(params, model, **kwargs)246        self.max_episodes = params.get('max_episodes', int(1E6))247        self.max_batch_episodes = params.get('max_batch_episodes', int(1E6))248        self.max_steps = params.get('max_steps', int(1E9))249        self.grad_repeats = params.get('grad_repeats', 1)250        self.batch_size = params.get('batch_size', 1e5)251        self.mini_batch_size = params.get('mini_batch_size', 200)252        self.controller = ACController(model, num_actions=params.get('num_actions'), params=params)253        self.controller = EpsilonGreedyController(controller=self.controller, params=params)254        self.runner = Heuristic_runner(self.controller, params=params)255        self.learner = BatchReinforceLearner(model, params=params) if learner is None else learner256        self.learner.set_controller(self.controller)257        self.opposd = params.get('opposd', False)258        self.opposd_iterations = params.get('opposd_iterations', 50)259    def get_transition_batch(self):260        transition_buffer = TransitionBatch(self.batch_size, self.runner.transition_format(), self.mini_batch_size)261        batch = self.runner.run(self.batch_size, transition_buffer)262        return batch263    def close(self):264        """ Overrides Experiment.close() """265        self.runner.close()266    def run(self, batch=None):267        """ Overrides Experiment.run() """268        # Plot past results if available269        if self.plot_frequency is not None and len(self.episode_losses) > 2:270            self.plot_training(update=True)271        # Run the experiment272        if not batch:273            batch = self.get_transition_batch()274        for e in range(self.max_batch_episodes):275            # Make a gradient update step276            self.learner.train(batch["buffer"])277            for _ in range(10):278                partial_result = self.test_in_env()279                self.episode_returns.append(partial_result)280            if self.plot_frequency is not None and (e + 1) % self.plot_frequency == 0:281                # self.plot_training(update=True)282                if self.print_when_plot:283                    print('Batch %u, epi-return %.4g +- %.3g' %284                          (len(self.episode_returns), np.mean(self.episode_returns[-10:]),285                           np.std(self.episode_returns[-10:])))286    def test_in_env(self):287        rewards = []288        for _ in range(10):289        # for _ in range(self.plot_frequency):290            state = self.runner.env.reset()291            done = False292            score = 0293            while not done:294                action = self.controller.choose(state, increase_counter=False).detach().item()295                # action = 1296                new_state, reward, done = self.runner.env.step(action)297                score += reward298                state = new_state299            rewards.append(score)300        return np.mean(score)301class ActorCriticExperimentRunning(Experiment):302    def __init__(self, params, model, learner=None, **kwargs):303        super().__init__(params, model, **kwargs)304        self.max_episodes = params.get('max_episodes', int(1E6))305        self.max_batch_episodes = params.get('max_batch_episodes', int(1E6))306        self.max_steps = params.get('max_steps', int(1E9))307        self.grad_repeats = params.get('grad_repeats', 1)308        self.batch_size = params.get('batch_size', 1024)309        self.controller = ACController(model, num_actions=params.get('num_actions'), params=params)310        self.controller = EpsilonGreedyController(controller=self.controller, params=params)311        # self.controller = Experiments_controller(controller=self.controller, params=params)312        self.runner = Experiments_runner(self.controller, params=params)313        self.learner = ReinforceLearner(model, params=params) if learner is None else learner314        self.learner.set_controller(self.controller)315        self.opposd = params.get('opposd', False)316    def close(self):317        """ Overrides Experiment.close() """318        self.runner.close()319    def run(self):320        """ Overrides Experiment.run() """321        # Run the experiment322        transition_buffer = TransitionBatch(self.batch_size, self.runner.transition_format(), self.batch_size)323        env_steps = 0 if len(self.env_steps) == 0 else self.env_steps[-1]324        interacted_episodes = 0325        for e in range(self.max_batch_episodes):326            # Run the policy for batch_size steps327            batch = self.runner.run(self.batch_size, transition_buffer)328            if self.opposd:329                for _ in range(50):330                    batch_w = self.runner.run(self.batch_size, transition_buffer)331                    self.learner.update_policy_distribution(batch_w['buffer'])332            # Make a gradient update step333            loss = self.learner.train(batch['buffer'])334            self.episode_losses.append(loss)335            # Quit if maximal number of environment steps is reached336            if env_steps >= self.max_steps:337                print('Steps limit reached')338                break339            self.episode_returns = np.append(self.episode_returns, np.mean(self.test_in_env()))340            self.plot_training(update=True)341            print('Batch %d %.4g +- %.3g' % (e, np.mean(self.episode_returns[-5:]),342                                             np.std(self.episode_returns[-5:])))343        if self.max_batch_episodes == 1:344            self.episode_returns = self.test_in_env()345    def plot_training(self, update=False):346        """ Plots logged training results. Use "update=True" if the plot is continuously updated347            or use "update=False" if this is the final call (otherwise there will be double plotting). """348        # Smooth curves349        # window = max(int(len(self.episode_returns) / 50), 10)350        window = max(int(len(self.episode_returns) / 20), 2)351        returns = np.convolve(self.episode_returns, np.ones(window) / window, 'valid')352        # Determine x-axis based on samples or episodes353        x_returns = [i + window for i in range(len(returns))]354        # Create plot355        colors = ['b', 'g', 'r']356        fig = plt.gcf()357        fig.set_size_inches(16, 4)358        plt.clf()359        pl.plot(x_returns, returns, colors[0])360        pl.xlabel('environment steps' if self.plot_train_samples else 'batch trainings')361        pl.ylabel('episode return')362        # dynamic plot update363        display.clear_output(wait=True)364        if update:365            display.display(pl.gcf())366    def test_in_env(self):367        rewards = []368        for _ in range(50):369        # for _ in range(self.plot_frequency):370            state = self.runner.env.reset()371            done = False372            score = 0373            while not done:374                action = self.controller.choose(state, increase_counter=False).detach().item()375                # action = 1376                new_state, reward, done = self.runner.env.step(action)377                score += reward378                state = new_state379            rewards.append(score)380        return rewards381class ActorCriticExperimentHeuristic(Experiment):382    def __init__(self, params, model, learner=None, **kwargs):383        super().__init__(params, model, **kwargs)384        self.max_episodes = params.get('max_episodes', int(1E6))385        self.max_batch_episodes = params.get('max_batch_episodes', int(1E6))386        self.max_steps = params.get('max_steps', int(1E9))387        self.grad_repeats = params.get('grad_repeats', 1)388        self.batch_size = params.get('batch_size', 1024)389        self.controller = ACController(model, num_actions=params.get('num_actions'), params=params)390        self.controller = EpsilonGreedyController(controller=self.controller, params=params)391        # self.controller = Experiments_controller(controller=self.controller, params=params)392        self.runner = Heuristic_runner(self.controller, params=params)393        self.learner = ReinforceLearner(model, params=params) if learner is None else learner394        self.learner.set_controller(self.controller)395    def run(self):396        """ Overrides Experiment.run() """397        # Plot past results if available398        if self.plot_frequency is not None and len(self.episode_losses) > 2:399            self.plot_training(update=True)400        # Run the experiment401        transition_buffer = TransitionBatch(self.batch_size, self.runner.transition_format(), self.batch_size)402        env_steps = 0 if len(self.env_steps) == 0 else self.env_steps[-1]403        interacted_episodes = 0404        for e in range(self.max_batch_episodes):405            # Run the policy for batch_size steps406            batch = self.runner.run(self.batch_size, transition_buffer)407            batch_episodes = 0408            loss = self.learner.train(batch['buffer'])409            self.episode_losses.append(loss)410            interacted_episodes += batch_episodes411            if interacted_episodes >= self.max_episodes:412                print('Environment interaction limit reached')413                break414            # Show intermediate results415            self.episode_returns = np.append(self.episode_returns, np.mean(self.test_in_env()))416            self.plot_training(update=True)417            print('Batch %d %.4g +- %.3g' % (e, np.mean(self.episode_returns[-5:]),418                                             np.std(self.episode_returns[-5:])))419        if self.max_batch_episodes == 1:420            self.episode_returns = self.test_in_env()421    def test_in_env(self):422        rewards = []423        for _ in range(50):424        # for _ in range(self.plot_frequency):425            state = self.runner.env.reset()426            done = False427            score = 0428            while not done:429                action = self.controller.choose(state, increase_counter=False).detach().item()430                # action = 1431                new_state, reward, done = self.runner.env.step(action)432                score += reward433                state = new_state434            rewards.append(score)435        return rewards...

afl.py

Source:afl.py

1import shutil2import subprocess3import sys4import time5import traceback6from pathlib import Path7from FTB.ProgramConfiguration import ProgramConfiguration8from FTB.Running.AutoRunner import AutoRunner9from .utils import (10    HAVE_FFPUPPET,11    apply_transform,12    setup_firefox,13    warn_local,14    write_stats_file,15)16def command_file_to_list(cmd_file):17    """18    Open and parse custom command line file19    @type cmd_file: String20    @param cmd_file: Command line file containing list of commands21    @rtype: Tuple22    @return: Test index in list and the command as a list of strings23    """24    cmdline = []25    idx = 026    test_idx = None27    with open(cmd_file) as cmd_fp:28        for line in cmd_fp:29            if "@@" in line:30                test_idx = idx31            cmdline.append(line.rstrip())32            idx += 133    return test_idx, cmdline34def scan_crashes(35    base_dir,36    collector,37    cmdline_path=None,38    env_path=None,39    test_path=None,40    firefox=None,41    firefox_prefs=None,42    firefox_extensions=None,43    firefox_testpath=None,44    transform=None,45):46    """47    Scan the base directory for crash tests and submit them to FuzzManager.48    @type base_dir: String49    @param base_dir: AFL base directory50    @type cmdline_path: String51    @param cmdline_path: Optional command line file to use instead of the52                         one found inside the base directory.53    @type env_path: String54    @param env_path: Optional file containing environment variables.55    @type test_path: String56    @param test_path: Optional filename where to copy the test before57                      attempting to reproduce a crash.58    @type transform: String59    @param transform: Optional path to script for applying post-crash60                      transformations.61    @rtype: int62    @return: Non-zero return code on failure63    """64    crash_dir = Path(base_dir) / "crashes"65    crash_files = []66    for crash_path in crash_dir.iterdir():67        # Ignore all files that aren't crash results68        if not crash_path.name.startswith("id:"):69            continue70        # Ignore our own status files71        if crash_path.suffix in {".submitted", ".failed"}:72            continue73        # Ignore files we already processed74        if (crash_dir / f"{crash_path.name}.submitted").exists() or (75            crash_dir / f"{crash_path.name}.failed"76        ).exists():77            continue78        crash_files.append(crash_path)79    if crash_files:80        # First try to read necessary information for reproducing crashes81        base_env = {}82        test_in_env = None83        if env_path:84            with open(env_path) as env_file:85                for line in env_file:86                    (name, val) = line.rstrip("\n").split("=", 1)87                    base_env[name] = val88                    if "@@" in val:89                        test_in_env = name90        if not cmdline_path:91            cmdline_path = Path(base_dir) / "cmdline"92        test_idx, cmdline = command_file_to_list(cmdline_path)93        if test_idx is not None:94            orig_test_arg = cmdline[test_idx]95        configuration = ProgramConfiguration.fromBinary(cmdline[0])96        if not configuration:97            raise Exception(98                "Creating program configuration from binary failed. "99                "Check your binary configuration file."100            )101        if firefox:102            (ffp, ff_cmd, ff_env) = setup_firefox(103                cmdline[0], firefox_prefs, firefox_extensions, firefox_testpath104            )105            cmdline = ff_cmd106            base_env.update(ff_env)107        for crash_file in crash_files:108            stdin = None109            env = None110            if base_env:111                env = dict(base_env)112            submission = crash_file113            if transform:114                try:115                    submission = Path(apply_transform(transform, crash_file))116                except Exception as exc:  # pylint: disable=broad-except117                    print(exc.args[1], file=sys.stderr)118            if test_idx is not None:119                cmdline[test_idx] = orig_test_arg.replace("@@", str(crash_file))120            elif test_in_env is not None:121                env[test_in_env] = env[test_in_env].replace("@@", str(crash_file))122            elif test_path is not None:123                shutil.copy(str(crash_file), test_path)124            else:125                stdin = crash_file.read_text()126            print(f"Processing crash file {crash_file}", file=sys.stderr)127            runner = AutoRunner.fromBinaryArgs(128                cmdline[0], cmdline[1:], env=env, stdin=stdin129            )130            if runner.run():131                crash_info = runner.getCrashInfo(configuration)132                collector.submit(crash_info, submission)133                (submission.parent / f"{submission.name}.submitted").touch()134                print("Success: Submitted crash to server.", file=sys.stderr)135            else:136                (submission.parent / f"{submission.name}.failed").touch()137                print(138                    "Error: Failed to reproduce the given crash, cannot submit.",139                    file=sys.stderr,140                )141        if firefox:142            ffp.clean_up()143def write_aggregated_stats_afl(base_dirs, outfile, cmdline_path=None):144    """145    Generate aggregated statistics from the given base directories146    and write them to the specified output file.147    @type base_dirs: list148    @param base_dirs: List of AFL base directories149    @type outfile: str150    @param outfile: Output file for aggregated statistics151    @type cmdline_path: String152    @param cmdline_path: Optional command line file to use instead of the153                         one found inside the base directory.154    """155    # Which fields to add156    wanted_fields_total = [157        "execs_done",158        "execs_per_sec",159        "pending_favs",160        "pending_total",161        "variable_paths",162        "unique_crashes",163        "unique_hangs",164    ]165    # Which fields to aggregate by mean166    wanted_fields_mean = ["exec_timeout"]167    # Which fields should be displayed per fuzzer instance168    wanted_fields_all = ["cycles_done", "bitmap_cvg"]169    # Which fields should be aggregated by max170    wanted_fields_max = ["last_path"]171    # Generate total list of fields to write172    fields = []173    fields.extend(wanted_fields_total)174    fields.extend(wanted_fields_mean)175    fields.extend(wanted_fields_all)176    fields.extend(wanted_fields_max)177    # Warnings to include178    warnings = []179    aggregated_stats = {}180    for field in wanted_fields_total:181        aggregated_stats[field] = 0182    for field in wanted_fields_mean:183        aggregated_stats[field] = (0, 0)184    for field in wanted_fields_all:185        aggregated_stats[field] = []186    def convert_num(num):187        if "." in num:188            return float(num)189        return int(num)190    for base_dir in base_dirs:191        stats_path = Path(base_dir) / "fuzzer_stats"192        if not cmdline_path:193            cmdline_path = Path(base_dir) / "cmdline"194        if stats_path.exists():195            stats = stats_path.read_text()196            for line in stats.splitlines():197                (field_name, field_val) = line.split(":", 1)198                field_name = field_name.strip()199                field_val = field_val.strip()200                if field_name in wanted_fields_total:201                    aggregated_stats[field_name] += convert_num(field_val)202                elif field_name in wanted_fields_mean:203                    (val, cnt) = aggregated_stats[field_name]204                    aggregated_stats[field_name] = (205                        val + convert_num(field_val),206                        cnt + 1,207                    )208                elif field_name in wanted_fields_all:209                    aggregated_stats[field_name].append(field_val)210                elif field_name in wanted_fields_max:211                    num_val = convert_num(field_val)212                    if (field_name not in aggregated_stats) or aggregated_stats[213                        field_name214                    ] < num_val:215                        aggregated_stats[field_name] = num_val216    # If we don't have any data here, then the fuzzers haven't written any statistics217    # yet218    if not aggregated_stats:219        return220    # Mean conversion221    for field_name in wanted_fields_mean:222        (val, cnt) = aggregated_stats[field_name]223        if cnt:224            aggregated_stats[field_name] = float(val) / float(cnt)225        else:226            aggregated_stats[field_name] = val227    # Verify fuzzmanagerconf exists and can be parsed228    _, cmdline = command_file_to_list(cmdline_path)229    target_binary = cmdline[0] if cmdline else None230    if target_binary is not None:231        if not Path(f"{target_binary}.fuzzmanagerconf").is_file():232            warnings.append(f"WARNING: Missing {target_binary}.fuzzmanagerconf\n")233        elif ProgramConfiguration.fromBinary(target_binary) is None:234            warnings.append(f"WARNING: Invalid {target_binary}.fuzzmanagerconf\n")235    # Look for unreported crashes236    failed_reports = 0237    for base_dir in base_dirs:238        crashes_dir = Path(base_dir) / "crashes"239        if not crashes_dir.is_dir():240            continue241        for crash_file in crashes_dir.iterdir():242            if crash_file.suffix == ".failed":243                failed_reports += 1244    if failed_reports:245        warnings.append(f"WARNING: Unreported crashes detected ({failed_reports})\n")246    # Write out data247    write_stats_file(outfile, fields, aggregated_stats, warnings)248def aflfuzz_main(opts, collector, s3m):249    assert not opts.cmd or opts.firefox250    if opts.firefox or opts.firefox_start_afl:251        assert HAVE_FFPUPPET252        assert not opts.custom_cmdline_file253        assert opts.firefox_prefs and opts.firefox_testpath254    if opts.firefox_start_afl:255        assert opts.aflbindir256        (ffp, cmd, env) = setup_firefox(257            opts.firefox_start_afl,258            opts.firefox_prefs,259            opts.firefox_extensions,260            opts.firefox_testpath,261        )262        afl_cmd = [str(Path(opts.aflbindir) / "afl-fuzz")]263        opts.rargs.remove("--")264        afl_cmd.extend(opts.rargs)265        afl_cmd.extend(cmd)266        try:267            subprocess.run(afl_cmd, env=env)268        except Exception:  # pylint: disable=broad-except269            traceback.print_exc()270        ffp.clean_up()271        return 0272    afl_out_dirs = []273    if opts.afloutdir:274        if not (Path(opts.afloutdir) / "crashes").exists():275            # The specified directory doesn't have a "crashes" sub directory.276            # Either the wrong directory was specified, or this is an AFL277            # multi-process synchronization directory. Try to figure this out here.278            for sync_dir in Path(opts.afloutdir).iterdir():279                if (sync_dir / "crashes").exists():280                    afl_out_dirs.append(str(sync_dir))281            if not afl_out_dirs:282                print(283                    f"Error: Directory {opts.afloutdir} does not appear to be a "284                    "valid AFL output/sync directory",285                    file=sys.stderr,286                )287                return 2288        else:289            afl_out_dirs.append(opts.afloutdir)290    # Upload and FuzzManager modes require specifying the AFL directory291    assert not (opts.s3_queue_upload or opts.fuzzmanager) or opts.afloutdir292    if opts.fuzzmanager or opts.s3_queue_upload or opts.aflstats:293        last_queue_upload = 0294        # If we reach this point, we know that AFL will be running on this machine,295        # so do the local warning check296        warn_local(opts)297        while True:298            if opts.fuzzmanager:299                for afl_out_dir in afl_out_dirs:300                    scan_crashes(301                        afl_out_dir,302                        collector,303                        opts.custom_cmdline_file,304                        opts.env_file,305                        opts.test_file,306                    )307            # Only upload queue files every 20 minutes308            if opts.s3_queue_upload and last_queue_upload < int(time.time()) - 1200:309                for afl_out_dir in afl_out_dirs:310                    s3m.upload_afl_queue_dir(afl_out_dir, new_cov_only=True)311                last_queue_upload = int(time.time())312            if opts.stats or opts.aflstats:313                write_aggregated_stats_afl(314                    afl_out_dirs,315                    opts.aflstats,316                    cmdline_path=opts.custom_cmdline_file,317                )318            time.sleep(10)...

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