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How to use init_scenario method in molecule

Best Python code snippet using molecule_python

test_strategies.py

Source:test_strategies.py

...198    if use_gpu:199        device = "cuda"200    else:201        device = "cpu"202    def init_scenario(self, multi_task=False):203        model = self.get_model(fast_test=True, multi_task=multi_task)204        optimizer = SGD(model.parameters(), lr=1e-3)205        criterion = CrossEntropyLoss()206        benchmark = self.load_benchmark(use_task_labels=multi_task)207        return model, optimizer, criterion, benchmark208    def test_naive(self):209        # SIT scenario210        model, optimizer, criterion, benchmark = self.init_scenario(211            multi_task=False)212        strategy = Naive(213            model,214            optimizer,215            criterion,216            train_mb_size=64,217            device=self.device,218            eval_mb_size=50,219            train_epochs=2,220        )221        self.run_strategy(benchmark, strategy)222        # MT scenario223        model, optimizer, criterion, benchmark = self.init_scenario(224            multi_task=True)225        strategy = Naive(226            model,227            optimizer,228            criterion,229            train_mb_size=64,230            device=self.device,231            eval_mb_size=50,232            train_epochs=2,233        )234        self.run_strategy(benchmark, strategy)235    def test_joint(self):236        class JointSTestPlugin(SupervisedPlugin):237            def __init__(self, benchmark):238                super().__init__()239                self.benchmark = benchmark240            def after_train_dataset_adaptation(241                    self, strategy: "SupervisedTemplate", **kwargs242            ):243                """244                Check that the dataset used for training contains the245                correct number of samples.246                """247                cum_len = sum(248                    [len(exp.dataset) for exp in self.benchmark.train_stream]249                )250                assert len(strategy.adapted_dataset) == cum_len251        # SIT scenario252        model, optimizer, criterion, benchmark = self.init_scenario(253            multi_task=False)254        strategy = JointTraining(255            model,256            optimizer,257            criterion,258            train_mb_size=64,259            device=self.device,260            eval_mb_size=50,261            train_epochs=2,262            plugins=[JointSTestPlugin(benchmark)],263        )264        strategy.evaluator.loggers = [TextLogger(sys.stdout)]265        strategy.train(benchmark.train_stream)266        # MT scenario267        model, optimizer, criterion, benchmark = self.init_scenario(268            multi_task=True)269        strategy = JointTraining(270            model,271            optimizer,272            criterion,273            train_mb_size=64,274            device=self.device,275            eval_mb_size=50,276            train_epochs=2,277            plugins=[JointSTestPlugin(benchmark)],278        )279        strategy.evaluator.loggers = [TextLogger(sys.stdout)]280        strategy.train(benchmark.train_stream)281        # Raise error when retraining282        self.assertRaises(283            AlreadyTrainedError,284            lambda: strategy.train(benchmark.train_stream),285        )286    def test_cwrstar(self):287        # SIT scenario288        model, optimizer, criterion, benchmark = self.init_scenario(289            multi_task=False)290        last_fc_name, _ = get_last_fc_layer(model)291        strategy = CWRStar(292            model,293            optimizer,294            criterion,295            last_fc_name,296            train_mb_size=64,297            device=self.device,298        )299        self.run_strategy(benchmark, strategy)300        dict_past_j = {}301        for cls in range(benchmark.n_classes):302            dict_past_j[cls] = 0303        # Check past_j SIT304        for exp in benchmark.train_stream:305            for cls in set(exp.dataset.targets):306                dict_past_j[cls] += exp.dataset.targets.count(cls)307        for cls in model.past_j.keys():308            assert model.past_j[cls] == dict_past_j[cls]309        for cls in model.past_j.keys():310            model.past_j[cls] = 0311        # MT scenario312        model, optimizer, criterion, benchmark = self.init_scenario(313            multi_task=True)314        strategy = CWRStar(315            model,316            optimizer,317            criterion,318            last_fc_name,319            train_mb_size=64,320            device=self.device,321        )322        # self.run_strategy(benchmark, strategy)323        # Check past_j MT324        dict_past_j = {}325        for cls in range(benchmark.n_classes):326            dict_past_j[cls] = 0327        for exp in benchmark.train_stream:328            for cls in set(exp.dataset.targets):329                dict_past_j[cls] += exp.dataset.targets.count(cls)330        for cls in model.past_j.keys():331            assert model.past_j[cls] == dict_past_j[cls]332    def test_replay(self):333        # SIT scenario334        model, optimizer, criterion, benchmark = self.init_scenario(335            multi_task=False)336        strategy = Replay(337            model,338            optimizer,339            criterion,340            mem_size=10,341            train_mb_size=64,342            device=self.device,343            eval_mb_size=50,344            train_epochs=2,345        )346        self.run_strategy(benchmark, strategy)347        # MT scenario348        model, optimizer, criterion, benchmark = self.init_scenario(349            multi_task=True)350        strategy = Replay(351            model,352            optimizer,353            criterion,354            mem_size=10,355            train_mb_size=64,356            device=self.device,357            eval_mb_size=50,358            train_epochs=2,359        )360        self.run_strategy(benchmark, strategy)361    def test_gdumb(self):362        # SIT scenario363        model, optimizer, criterion, benchmark = self.init_scenario(364            multi_task=False)365        strategy = GDumb(366            model,367            optimizer,368            criterion,369            mem_size=200,370            train_mb_size=64,371            device=self.device,372            eval_mb_size=50,373            train_epochs=2,374        )375        self.run_strategy(benchmark, strategy)376        # MT scenario377        model, optimizer, criterion, benchmark = self.init_scenario(378            multi_task=True)379        strategy = GDumb(380            model,381            optimizer,382            criterion,383            mem_size=200,384            train_mb_size=64,385            device=self.device,386            eval_mb_size=50,387            train_epochs=2,388        )389        self.run_strategy(benchmark, strategy)390    def test_cumulative(self):391        # SIT scenario392        model, optimizer, criterion, benchmark = self.init_scenario(393            multi_task=False)394        strategy = Cumulative(395            model,396            optimizer,397            criterion,398            train_mb_size=64,399            device=self.device,400            eval_mb_size=50,401            train_epochs=2,402        )403        self.run_strategy(benchmark, strategy)404        # MT scenario405        model, optimizer, criterion, benchmark = self.init_scenario(406            multi_task=True)407        strategy = Cumulative(408            model,409            optimizer,410            criterion,411            train_mb_size=64,412            device=self.device,413            eval_mb_size=50,414            train_epochs=2,415        )416        self.run_strategy(benchmark, strategy)417    def test_slda(self):418        model, _, criterion, benchmark = self.init_scenario(multi_task=False)419        strategy = StreamingLDA(420            model,421            criterion,422            input_size=10,423            output_layer_name="features",424            num_classes=10,425            eval_mb_size=7,426            train_epochs=1,427            device=self.device,428            train_mb_size=7,429        )430        self.run_strategy(benchmark, strategy)431    def test_warning_slda_lwf(self):432        model, _, criterion, benchmark = self.init_scenario(multi_task=False)433        with self.assertWarns(Warning) as cm:434            StreamingLDA(435                model,436                criterion,437                input_size=10,438                output_layer_name="features",439                num_classes=10,440                plugins=[LwFPlugin(), ReplayPlugin()],441            )442    def test_lwf(self):443        # SIT scenario444        model, optimizer, criterion, benchmark = self.init_scenario(445            multi_task=False)446        strategy = LwF(447            model,448            optimizer,449            criterion,450            alpha=[0, 1 / 2, 2 * (2 / 3), 3 * (3 / 4), 4 * (4 / 5)],451            temperature=2,452            device=self.device,453            train_mb_size=10,454            eval_mb_size=50,455            train_epochs=2,456        )457        self.run_strategy(benchmark, strategy)458        # MT scenario459        model, optimizer, criterion, benchmark = self.init_scenario(460            multi_task=True)461        strategy = LwF(462            model,463            optimizer,464            criterion,465            alpha=[0, 1 / 2, 2 * (2 / 3), 3 * (3 / 4), 4 * (4 / 5)],466            temperature=2,467            device=self.device,468            train_mb_size=10,469            eval_mb_size=50,470            train_epochs=2,471        )472        self.run_strategy(benchmark, strategy)473    def test_agem(self):474        # SIT scenario475        model, optimizer, criterion, benchmark = self.init_scenario(476            multi_task=False)477        strategy = AGEM(478            model,479            optimizer,480            criterion,481            patterns_per_exp=25,482            sample_size=25,483            train_mb_size=10,484            eval_mb_size=50,485            train_epochs=2,486        )487        self.run_strategy(benchmark, strategy)488        # MT scenario489        model, optimizer, criterion, benchmark = self.init_scenario(490            multi_task=True)491        strategy = AGEM(492            model,493            optimizer,494            criterion,495            patterns_per_exp=25,496            sample_size=25,497            train_mb_size=10,498            eval_mb_size=50,499            train_epochs=2,500        )501        self.run_strategy(benchmark, strategy)502    def test_gem(self):503        # SIT scenario504        model, optimizer, criterion, benchmark = self.init_scenario(505            multi_task=False)506        strategy = GEM(507            model,508            optimizer,509            criterion,510            patterns_per_exp=256,511            train_mb_size=10,512            eval_mb_size=50,513            train_epochs=2,514        )515        self.run_strategy(benchmark, strategy)516        # MT scenario517        model, optimizer, criterion, benchmark = self.init_scenario(518            multi_task=True)519        strategy = GEM(520            model,521            optimizer,522            criterion,523            patterns_per_exp=256,524            train_mb_size=10,525            eval_mb_size=50,526            train_epochs=2,527        )528        benchmark = self.load_benchmark(use_task_labels=True)529        self.run_strategy(benchmark, strategy)530    def test_ewc(self):531        # SIT scenario532        model, optimizer, criterion, benchmark = self.init_scenario(533            multi_task=False)534        strategy = EWC(535            model,536            optimizer,537            criterion,538            ewc_lambda=0.4,539            mode="separate",540            train_mb_size=10,541            eval_mb_size=50,542            train_epochs=2,543        )544        self.run_strategy(benchmark, strategy)545        # MT scenario546        model, optimizer, criterion, benchmark = self.init_scenario(547            multi_task=True)548        strategy = EWC(549            model,550            optimizer,551            criterion,552            ewc_lambda=0.4,553            mode="separate",554            train_mb_size=10,555            eval_mb_size=50,556            train_epochs=2,557        )558        self.run_strategy(benchmark, strategy)559    def test_ewc_online(self):560        # SIT scenario561        model, optimizer, criterion, benchmark = self.init_scenario(562            multi_task=False)563        strategy = EWC(564            model,565            optimizer,566            criterion,567            ewc_lambda=0.4,568            mode="online",569            decay_factor=0.1,570            train_mb_size=10,571            eval_mb_size=50,572            train_epochs=2,573        )574        self.run_strategy(benchmark, strategy)575        # # MT scenario576        # model, optimizer, criterion, benchmark = self.init_scenario(577        #     multi_task=True)578        # strategy = EWC(579        #     model,580        #     optimizer,581        #     criterion,582        #     ewc_lambda=0.4,583        #     mode="online",584        #     decay_factor=0.1,585        #     train_mb_size=10,586        #     eval_mb_size=50,587        #     train_epochs=2,588        # )589        # self.run_strategy(benchmark, strategy)590    def test_rwalk(self):591        # SIT scenario592        model, optimizer, criterion, benchmark = self.init_scenario(593            multi_task=False)594        strategy = Naive(595            model,596            optimizer,597            criterion,598            train_mb_size=10,599            eval_mb_size=50,600            train_epochs=2,601            plugins=[602                RWalkPlugin(603                    ewc_lambda=0.1,604                    ewc_alpha=0.9,605                    delta_t=10,606                ),607            ],608        )609        self.run_strategy(benchmark, strategy)610        # # MT scenario611        # model, optimizer, criterion, benchmark = self.init_scenario(612        #     multi_task=True)613        # strategy = Naive(614        #     model,615        #     optimizer,616        #     criterion,617        #     train_mb_size=10,618        #     eval_mb_size=50,619        #     train_epochs=2,620        #     plugins=[621        #         RWalkPlugin(622        #             ewc_lambda=0.1,623        #             ewc_alpha=0.9,624        #             delta_t=10,625        #         ),626        #     ],627        # )628        # self.run_strategy(benchmark, strategy)629    def test_synaptic_intelligence(self):630        # SIT scenario631        model, optimizer, criterion, benchmark = self.init_scenario(632            multi_task=False)633        strategy = SynapticIntelligence(634            model,635            optimizer,636            criterion,637            si_lambda=0.0001,638            train_epochs=1,639            train_mb_size=10,640            eval_mb_size=10,641        )642        self.run_strategy(benchmark, strategy)643        # MT scenario644        # model, optimizer, criterion, benchmark = self.init_scenario(645        #     multi_task=True)646        # strategy = SynapticIntelligence(647        #     model,648        #     optimizer,649        #     criterion,650        #     si_lambda=0.0001,651        #     train_epochs=1,652        #     train_mb_size=10,653        #     eval_mb_size=10,654        # )655        # self.run_strategy(benchmark, strategy)656    def test_cope(self):657        # Fast benchmark (hardcoded)658        n_classes = 10659        emb_size = n_classes  # Embedding size660        # SIT scenario661        model, optimizer, criterion, benchmark = self.init_scenario(662            multi_task=False)663        strategy = CoPE(664            model,665            optimizer,666            criterion,667            mem_size=10,668            n_classes=n_classes,669            p_size=emb_size,670            train_mb_size=10,671            device=self.device,672            eval_mb_size=50,673            train_epochs=2,674        )675        self.run_strategy(benchmark, strategy)676        # MT scenario677        # model, optimizer, criterion, benchmark = self.init_scenario(678        #     multi_task=True)679        # strategy = CoPE(680        #     model,681        #     optimizer,682        #     criterion,683        #     mem_size=10,684        #     n_classes=n_classes,685        #     p_size=emb_size,686        #     train_mb_size=10,687        #     device=self.device,688        #     eval_mb_size=50,689        #     train_epochs=2,690        # )691        # self.run_strategy(benchmark, strategy)692    def test_pnn(self):693        # only multi-task scenarios.694        # eval on future tasks is not allowed.695        model = PNN(num_layers=3, in_features=6, hidden_features_per_column=10)696        optimizer = torch.optim.SGD(model.parameters(), lr=0.1)697        strategy = PNNStrategy(698            model,699            optimizer,700            train_mb_size=10,701            device=self.device,702            eval_mb_size=50,703            train_epochs=2,704        )705        # train and test loop706        benchmark = self.load_benchmark(use_task_labels=True)707        for train_task in benchmark.train_stream:708            strategy.train(train_task)709        strategy.eval(benchmark.test_stream)710    def test_icarl(self):711        model, optimizer, criterion, benchmark = self.init_scenario(712            multi_task=False)713        strategy = ICaRL(714            model.features,715            model.classifier,716            optimizer,717            20,718            buffer_transform=None,719            criterion=criterion,720            fixed_memory=True,721            train_mb_size=10,722            train_epochs=2,723            eval_mb_size=50,724            device=self.device,725        )726        self.run_strategy(benchmark, strategy)727    def test_lfl(self):728        # SIT scenario729        model, optimizer, criterion, benchmark = self.init_scenario(730            multi_task=False)731        strategy = LFL(732            model,733            optimizer,734            criterion,735            lambda_e=0.0001,736            train_mb_size=10,737            device=self.device,738            eval_mb_size=50,739            train_epochs=2,740        )741        self.run_strategy(benchmark, strategy)742        # MT scenario743        # model, optimizer, criterion, benchmark = self.init_scenario(744        #     multi_task=True)745        # strategy = LFL(746        #     model,747        #     optimizer,748        #     criterion,749        #     lambda_e=0.0001,750        #     train_mb_size=10,751        #     device=self.device,752        #     eval_mb_size=50,753        #     train_epochs=2,754        # )755        # self.run_strategy(benchmark, strategy)756    def test_mas(self):757        # SIT scenario758        model, optimizer, criterion, benchmark = self.init_scenario(759            multi_task=False)760        strategy = MAS(761            model,762            optimizer,763            criterion,764            lambda_reg=1.0,765            alpha=0.5,766            train_mb_size=10,767            device=self.device,768            eval_mb_size=50,769            train_epochs=2,770        )771        self.run_strategy(benchmark, strategy)772        # MT scenario773        # model, optimizer, criterion, benchmark = self.init_scenario(774        #     multi_task=True)775        # strategy = MAS(776        #     model,777        #     optimizer,778        #     criterion,779        #     lambda_reg=1.0,780        #     alpha=0.5,781        #     train_mb_size=10,782        #     device=self.device,783        #     eval_mb_size=50,784        #     train_epochs=2,785        # )786        # self.run_strategy(benchmark, strategy)787    def load_benchmark(self, use_task_labels=False):...

test_node.py

Source:test_node.py

...3from typing import Tuple, List, Dict, Optional, Set4from .node import Node, INVALID_SHARE5from .crypto import normalize, add, multiply, G1, H1, G2, H26from . import crypto7def init_scenario(8    n: int = 5, t: int = 2, use_random_indices: bool = True9) -> Tuple[int, int, List[Node]]:10    nodes = [Node() for _ in range(n)]11    indices = list(range(1, n + 1))12    if use_random_indices:13        indices = random.sample(range(1, 10000), n)14    public_keys = {idx: node.public_key for idx, node in zip(indices, nodes)}15    for idx, node in zip(indices, nodes):16        node.setup(n, t, idx, public_keys)17    return n, t, nodes18def compute_and_distribute_shares(19    nodes: List[Node],20    invalid_shares_from_to: Optional[Set[Tuple[Node, Node]]] = None,21    invalid_commitments_from: Optional[Set[Node]] = None,22    do_not_distribute_from: Optional[Set[Node]] = None,23):24    invalid_shares_from_to = invalid_shares_from_to or set()25    invalid_commitments_from = invalid_commitments_from or set()26    do_not_distribute_from = do_not_distribute_from or set()27    all_encrypted_shares = {}28    all_commitments = {}29    for node in nodes:30        all_encrypted_shares[node.idx], all_commitments[node.idx] = node.compute_shares()31    for issuer in nodes:32        if issuer in invalid_commitments_from:33            all_commitments[issuer.idx][0] = multiply(all_commitments[issuer.idx][0], 2)34        for receiver in nodes:35            if (issuer, receiver) in invalid_shares_from_to:36                assert issuer.idx != receiver.idx, "error in testcase"37                all_encrypted_shares[issuer.idx][receiver.idx] += 138    for issuer in nodes:39        if issuer in do_not_distribute_from:40            continue41        for receiver in nodes:42            if issuer.idx == receiver.idx:43                continue44            ok = receiver.load_shares(45                issuer.idx, all_encrypted_shares[issuer.idx], all_commitments[issuer.idx]46            )47            if (issuer, receiver) in invalid_shares_from_to or issuer in invalid_commitments_from:48                assert not ok, "invalid share should be detected"49            else:50                assert ok, "verification should pass, since the share is valid"51def test_shared_keys():52    n, t, nodes = init_scenario()53    n1, n2, *_ = nodes54    assert normalize(n1.shared_keys[n2.idx]) == normalize(n2.shared_keys[n1.idx])55def test_share_distribution_all_fine_case():56    n, t, nodes = init_scenario()57    compute_and_distribute_shares(nodes)58def test_share_distribution_node_invalid_shares_should_be_detected():59    n, t, nodes = init_scenario()60    n1, n2, n3, *_ = nodes61    compute_and_distribute_shares(nodes, invalid_shares_from_to={(n1, n2), (n1, n3), (n3, n1)})62def test_share_distribution_node_invalid_commitments_should_be_detected():63    n, t, nodes = init_scenario()64    n1, n2, *_ = nodes65    compute_and_distribute_shares(nodes, invalid_commitments_from={n1, n2})66def compute_and_distribute_disputes(nodes):67    all_disputes = {node.idx: node.compute_disputes() for node in nodes}68    for node in nodes:69        for disputer_idx, disputes in all_disputes.items():70            for issuer_idx, dispute in disputes.items():71                assert node.load_dispute(issuer_idx, disputer_idx, *dispute)72def test_valid_dispute_accepted():73    n, t, nodes = init_scenario()74    n1, n2, *_ = nodes75    compute_and_distribute_shares(nodes, invalid_shares_from_to={(n1, n2)})76    disputes = n2.compute_disputes()77    assert len(disputes) == 178    for node in nodes:79        assert node is n2 or len(node.compute_disputes()) == 080    dispute = disputes[n1.idx]81    for node in nodes:82        assert node.load_dispute(n1.idx, n2.idx, *dispute)83def test_invalid_dispute_rejected__invalid_key():84    n, t, nodes = init_scenario()85    n1, n2, *_ = nodes86    compute_and_distribute_shares(nodes, invalid_shares_from_to={(n1, n2)})87    dispute = n2.compute_disputes()[n1.idx]88    shared_key, proof = dispute89    shared_key = multiply(shared_key, 4711)90    for node in nodes:91        assert not node.load_dispute(n1.idx, n2.idx, shared_key, proof)92def test_invalid_dispute_rejected__invalid_key_proof():93    n, t, nodes = init_scenario()94    n1, n2, *_ = nodes95    compute_and_distribute_shares(nodes, invalid_shares_from_to={(n1, n2)})96    dispute = n2.compute_disputes()[n1.idx]97    shared_key, proof = dispute98    proof = proof[0], proof[1] + 471199    for node in nodes:100        assert not node.load_dispute(n1.idx, n2.idx, shared_key, proof)101def test_invalid_dispute_rejected__share_valid():102    n, t, nodes = init_scenario()103    n1, n2, *_ = nodes104    compute_and_distribute_shares(nodes)105    # wrongly mark share as invalid so that the node actually creates a dispute106    n2.decrypted_shares[n1.idx] = INVALID_SHARE107    dispute = n2.compute_disputes()[n1.idx]108    for node in nodes:109        assert not node.load_dispute(n1.idx, n2.idx, *dispute)110def test_qualified_nodes__all():111    n, t, nodes = init_scenario()112    compute_and_distribute_shares(nodes)113    compute_and_distribute_disputes(nodes)114    for node in nodes:115        assert node.compute_qualified_nodes() == [node.idx for node in nodes]116def test_qualified_nodes__exclude_disputed():117    n, t, nodes = init_scenario()118    n1, n2, *_ = nodes119    compute_and_distribute_shares(nodes, invalid_shares_from_to={(n1, n2)})120    compute_and_distribute_disputes(nodes)121    qualified_nodes = [node.idx for node in nodes if node is not n1]122    for node in nodes:123        assert node.compute_qualified_nodes() == qualified_nodes124def test_qualified_nodes__exclude_undistributed():125    n, t, nodes = init_scenario()126    n1, *_ = nodes127    compute_and_distribute_shares(nodes, do_not_distribute_from={n1})128    compute_and_distribute_disputes(nodes)129    qualified_nodes = [node.idx for node in nodes if node is not n1]130    for node in nodes:131        if node is not n1:132            assert node.compute_qualified_nodes() == qualified_nodes133def compute_and_distribute_key_shares(nodes):134    for node in nodes:135        node.compute_qualified_nodes()136    all_key_shares = {node.idx: node.compute_key_share() for node in nodes}137    for receiving_node in nodes:138        for issuer_idx, key_shares in all_key_shares.items():139            assert receiving_node.load_key_share(issuer_idx, *key_shares)140def test_key_shares_verification__all_correct():141    n, t, nodes = init_scenario()142    compute_and_distribute_shares(nodes)143    compute_and_distribute_disputes(nodes)144    compute_and_distribute_key_shares(nodes)145def test_key_shares_verification__invalid():146    n, t, nodes = init_scenario()147    compute_and_distribute_shares(nodes)148    compute_and_distribute_disputes(nodes)149    for node in nodes:150        node.compute_qualified_nodes()151        node.compute_key_share()152    n1, n2, *_ = nodes153    h1 = multiply(H1, n1.secret)154    h1_proof = crypto.dleq(H1, h1, G1, n1.commitments[n1.idx][0], n1.secret)155    h2 = multiply(H2, n1.secret)156    assert n2.load_key_share(n1.idx, h1, h1_proof, h2)157    h1 = multiply(H1, n1.secret)158    h1_proof = crypto.dleq(159        multiply(H1, 2), h1, multiply(G1, 2), n1.commitments[n1.idx][0], n1.secret160    )161    h2 = multiply(H2, n1.secret)162    assert not n2.load_key_share(n1.idx, h1, h1_proof, h2)163    h1 = multiply(H1, n1.secret + 1)164    h1_proof = crypto.dleq(H1, h1, G1, n1.commitments[n1.idx][0], n1.secret)165    h2 = multiply(H2, n1.secret + 1)166    assert not n2.load_key_share(n1.idx, h1, h1_proof, h2)167    h1 = multiply(H1, n1.secret)168    h1_proof = crypto.dleq(H1, h1, G1, n1.commitments[n1.idx][0], n1.secret)169    h2 = multiply(H2, n1.secret + 1)170    assert not n2.load_key_share(n1.idx, h1, h1_proof, h2)171def test_key_shares_recovery():172    n, t, nodes = init_scenario()173    n1, n2, *_ = nodes174    compute_and_distribute_shares(nodes)175    compute_and_distribute_disputes(nodes)176    compute_and_distribute_key_shares(nodes)177    recs_for_n1 = {178        node.idx: node.initiate_key_share_recovery(n1.idx) for node in nodes if node is not n1179    }180    for verifier in nodes:181        if verifier is n1:182            continue183        for recoverer_idx, rec in recs_for_n1.items():184            assert verifier.load_recovered_key_share(n1.idx, recoverer_idx, *rec)185    for verifier in nodes:186        if verifier is n1:187            continue188        x = verifier.key_shares189        assert verifier.recover_key_share(n1.idx)190        assert x == verifier.key_shares191def test_master_key_derivation():192    n, t, nodes = init_scenario()193    n1, *_ = nodes194    compute_and_distribute_shares(nodes)195    compute_and_distribute_disputes(nodes)196    compute_and_distribute_key_shares(nodes)197    for node in nodes:198        node.derive_master_public_key()199    mk = normalize(n1.master_public_key)200    for node in nodes:201        assert mk == normalize(node.master_public_key)202def test_group_key_derivation():203    n, t, nodes = init_scenario()204    n1, *_ = nodes205    compute_and_distribute_shares(nodes)206    compute_and_distribute_disputes(nodes)207    compute_and_distribute_key_shares(nodes)208    for node in nodes:209        node.derive_group_keys()210    for node in nodes:211        assert n1.verify_group_public_key(212            node.idx,213            node.group_public_key,214            node.group_public_key_in_G1,215            node.group_public_key_correctness_proof,...

benchmark_utils.py

Source:benchmark_utils.py

...26            print("Run number {}/{} ({}/{} succesfull): dimension = {}, nclients = {}, dropout = {}".format(27                counter, total, success[True], success[True] + success[False], dimension, nclients, dropout))28            29            scenario = Scenario(dimension, inputsize, keysize, ceil(threshold*nclients), nclients, dropout)30            clients, server = init_scenario(scenario)31            valid = benchmark(clients, server, scenario, REPS)32            success[valid] += 133    nclients = 600    34    for dimension in dimensions:35        for dropout in dropouts:36            counter += 137            if dont_run: 38                print("run {}: dimension = {}, nclients = {}, dropout = {}".format(counter, dimension, nclients, dropout))39                continue40            if runs and counter not in runs:41                continue42            43            print("Run number {}/{} ({}/{} succesfull): dimension = {}, nclients = {}, dropout = {}".format(44                counter, total, success[True], success[True] + success[False], dimension, nclients, dropout))45            46            47            scenario = Scenario(dimension, inputsize, keysize, ceil(threshold*nclients), nclients, dropout)48            clients, server = init_scenario(scenario)49            valid = benchmark(clients, server, scenario, REPS)    50            success[valid] += 1...

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