How to use initialize_plugins method in avocado

Best Python code snippet using avocado_python

methods.py

Source:methods.py

...77        self._model = create_instance(model)78        self._model.fc = nn.Linear(2048, 7, bias=False)79        self._optimizer = create_instance(optimizer, params=self._model.parameters())80        self._criterion = create_instance(criterion) 81        self._plugins = self.initialize_plugins()82    83    @property84    def model(self):85        return self._model86    87    @property88    def optimizer(self):89        return self._optimizer90    91    @property92    def criterion(self):93        return self._criterion94    95    @property96    def plugins(self):97        return self._plugins98    99    def initialize_plugins(self):100        return ClassStrategyPlugin()101    102    103class EWC(object):104    def __init__(self, 105                 model: edict,106                 optimizer: edict,107                 criterion: edict108                 ):109        110        self._model = create_instance(model)111        self._model.fc = nn.Linear(2048, 7, bias=False)112        self._optimizer = create_instance(optimizer, params=self._model.parameters())113        self._criterion = create_instance(criterion) 114        self._plugins = self.initialize_plugins()115    116    @property117    def model(self):118        return self._model119    120    @property121    def optimizer(self):122        return self._optimizer123    124    @property125    def criterion(self):126        return self._criterion127    128    @property129    def plugins(self):130        return self._plugins131    132    def initialize_plugins(self):133        return EWCPlugin(ewc_lambda=0.4, decay_factor=0.1, mode="online")134class Replay(object):135    def __init__(self, 136                 model: edict,137                 optimizer: edict,138                 criterion: edict,139                 mem_size: int,140                 storage_policy: dict,141                 selection_strategy: dict=None,142                 features_based: bool=False143                 ):144        145        self._model = create_instance(model)146        self._model.fc = nn.Linear(2048, 7, bias=False)147        self._optimizer = create_instance(optimizer, params=self._model.parameters())148        self._criterion = create_instance(criterion) 149        self._mem_size = mem_size150        151        # if features based exemplar strategy152        # feeding model and its layername for features extractor153        if features_based:154            self._selection_strategy = create_instance(selection_strategy, model=self._model) if not selection_strategy is None else None155        else:    156            self._selection_strategy = create_instance(selection_strategy) if not selection_strategy is None else None157        158        # create storage policy with selection strategy if pre-defined159        if self._selection_strategy:160            try:161                self._storage_policy = create_instance(storage_policy, selection_strategy=self._selection_strategy)162                self._storage_policy.ext_mem = dict()163                print(f"selection strategy: {self._selection_strategy}")164            except:165                self._storage_policy = create_instance(storage_policy)166                self._storage_policy.ext_mem = dict()167                print(f"Selection strategy: None")168        else:169            print(f"Selection strategy: None")170            self._storage_policy = create_instance(storage_policy)171            self._storage_policy.ext_mem = dict()172            173        self._plugins = self.initialize_plugins()174    175    @property176    def model(self):177        return self._model178    179    @property180    def optimizer(self):181        return self._optimizer182    183    @property184    def criterion(self):185        return self._criterion186    187    @property188    def plugins(self):189        return self._plugins190    191    def initialize_plugins(self):192        return ReplayPlugin(self._mem_size, self._storage_policy)193    194    195class SynapticIntelligence(object):196    def __init__(self, 197                 model: edict,198                 optimizer: edict,199                 criterion: edict,200                 si_lambda: float,201                 excluded_parameters: str="fc"202                 ):203        204        self._model = create_instance(model)205        self._model.fc = nn.Linear(2048, 7, bias=False)206        self._optimizer = create_instance(optimizer, params=self._model.parameters())207        self._criterion = create_instance(criterion) 208        self._si_lambda = si_lambda209        self._excluded_parameters = excluded_parameters210        self._plugins = self.initialize_plugins()211    212    @property213    def model(self):214        return self._model215    216    @property217    def optimizer(self):218        return self._optimizer219    220    @property221    def criterion(self):222        return self._criterion223    224    @property225    def plugins(self):226        return self._plugins227    228    def initialize_plugins(self):229        return SynapticIntelligencePlugin(si_lambda=self._si_lambda, 230                                          excluded_parameters=self._excluded_parameters)231        232class AGEM(object):233    def __init__(self, 234                 model: edict,235                 optimizer: edict,236                 criterion: edict,237                 **kwargs238                 ):239        240        self._model = create_instance(model)241        self._model.fc = nn.Linear(2048, 7, bias=False)242        self._optimizer = create_instance(optimizer, params=self._model.parameters())243        self._criterion = create_instance(criterion) 244        self._kwargs = kwargs245        self._plugins = self.initialize_plugins()246    247    @property248    def model(self):249        return self._model250    251    @property252    def optimizer(self):253        return self._optimizer254    255    @property256    def criterion(self):257        return self._criterion258    259    @property260    def plugins(self):261        return self._plugins262    263    def initialize_plugins(self):264        return AGEMPlugin(**self._kwargs)265    266    267    268class LwF(object):269    def __init__(self, 270                 model: edict,271                 optimizer: edict,272                 criterion: edict,273                 ):274        275        self._model = create_instance(model)276        self._model.fc = nn.Linear(2048, 7, bias=False)277        self._optimizer = create_instance(optimizer, params=self._model.parameters())278        self._criterion = create_instance(criterion) 279        self._plugins = self.initialize_plugins()280    281    @property282    def model(self):283        return self._model284    285    @property286    def optimizer(self):287        return self._optimizer288    289    @property290    def criterion(self):291        return self._criterion292    293    @property294    def plugins(self):295        return self._plugins296    297    def initialize_plugins(self):298        return LwFPlugin()299    300    301    302class CoPE(object):303    def __init__(self, 304                 model: edict,305                 optimizer: edict,306                 criterion: edict,307                 **kwargs308                 ):309        310        self._model = create_instance(model)311        self._model.fc = nn.Linear(2048, 7, bias=False)312        self._optimizer = create_instance(optimizer, params=self._model.parameters())313        self._criterion = create_instance(criterion) 314        self._kwargs = kwargs315        self._plugins = self.initialize_plugins()316    317    @property318    def model(self):319        return self._model320    321    @property322    def optimizer(self):323        return self._optimizer324    325    @property326    def criterion(self):327        return self._criterion328    329    @property330    def plugins(self):331        return self._plugins332    333    def initialize_plugins(self):334        return CoPEPlugin(**self._kwargs)335    336    337class CWRStar(object):338    def __init__(self, 339                 model: edict,340                 optimizer: edict,341                 criterion: edict,342                 **kwargs343                 ):344        345        self._model = create_instance(model)346        self._model.fc = nn.Linear(2048, 7, bias=False)347        self._optimizer = create_instance(optimizer, params=self._model.parameters())348        self._criterion = create_instance(criterion) 349        self._kwargs = kwargs350        self._plugins = self.initialize_plugins()351    352    @property353    def model(self):354        return self._model355    356    @property357    def optimizer(self):358        return self._optimizer359    360    @property361    def criterion(self):362        return self._criterion363    364    @property365    def plugins(self):366        return self._plugins367    368    def initialize_plugins(self):...

detect_secrets.py

Source:detect_secrets.py

...65    66        lineNum += 167    68    return json.dumps(secrets)69def initialize_plugins(hex_limit, base64_limit,exclude_lines_regex=None):70    plugins = []71    for pluginClass in import_plugins(()).values():72        plugins.append(pluginClass(base64_limit=base64_limit,hex_limit=hex_limit, exclude_lines_regex=exclude_lines_regex))73    return tuple(plugins)74    75def lambda_handler(event, context):76    # TODO implement77    78    try:79        data = event["data"]80    except KeyError as e:81        errorMsg = "Error(s): Attribute " + str(e) + " is missing in input data"82        return {83            'result': errorMsg84        }85        86    #initialise plugins87    try:88        exclude_lines_regex = None89        90        if("exclude_lines_regex" in event):91            exclude_lines_regex=event["exclude_lines_regex"]92        93        94        if("base64_limit" in event and "hex_limit" in event):95            plugins = initialize_plugins(hex_limit=event["hex_limit"],base64_limit=event["base64_limit"],exclude_lines_regex=exclude_lines_regex)96        elif("base64_limit" in event):97            plugins = initialize_plugins(base64_limit=event["base64_limit"],hex_limit=hex_limit,exclude_lines_regex=exclude_lines_regex)98        elif("hex_limit" in event):99            plugins = initialize_plugins(hex_limit=event["hex_limit"],base64_limit=base64_limit,exclude_lines_regex=exclude_lines_regex)100        else:101            plugins = initialize_plugins(base64_limit=base64_limit,hex_limit=hex_limit, exclude_lines_regex=exclude_lines_regex)102        103    except ValueError as e:104        return {105            'result': "Error(s): invalid hex_limt/base64_limit" 106        }107    except TypeError as e:108        return {109            'result': "Error(s): " + str(e)110        }111        112    if("mode" in event):113        if(event['mode'] == 'debug'):114            result = scan_secrets(data,plugins)115            return {116                'result' : result117            }118    result = scan_secrets_counts(data,plugins)119        120    return {121        'result': result122       123    }124# if __name__ == "__main__":125#     event = {126#   "data": "AWSsecret = AKIA1212121212121212\nsecret = AYje346w846mgvitmon2amz02awa8bjg3g",127#   "hex_limit": 1,128#  "base64_limit": 1,129#  "exclude_lines_regex" : "AKIA"130# }131#     x = lambda_handler(event,"")132#     print(x)133#     # print(x)134#     # plugins = initialize_plugins(1,1, "12")135#     # x = 'AWSsecret = AKIA1212121212121212\nsecret = AYje346w846mgvitmon2amz02awa8bjg3g'136#     # result = scan_screts(x, plugins)137#     # print(result)...

__init__.py

Source:__init__.py

...11    app = initialize_flask_app()12    app.config.from_object(config)13    with app.app_context():14        # Initialize Plugins15        initialize_plugins(app)16        # Register blueprints17        register_blueprints(app)18        register_restful_api(app)19        configure_database(app)...

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