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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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