How to use create_stage method in localstack

Best Python code snippet using localstack_python

dope_model.py

Source:dope_model.py

...37            str(i_layer + 2), nn.Conv2d(256, 128, kernel_size=3, stride=1, padding=1))38        self.vgg.add_module(str(i_layer + 3), nn.ReLU(inplace=True))39        # print('---Belief------------------------------------------------')40        # _2 are the belief map stages41        self.m1_2 = DopeNetwork.create_stage(128, numBeliefMap, True)42        self.m2_2 = DopeNetwork.create_stage(128 + numBeliefMap + numAffinity, numBeliefMap, False)43        self.m3_2 = DopeNetwork.create_stage(128 + numBeliefMap + numAffinity, numBeliefMap, False)44        self.m4_2 = DopeNetwork.create_stage(128 + numBeliefMap + numAffinity, numBeliefMap, False)45        self.m5_2 = DopeNetwork.create_stage(128 + numBeliefMap + numAffinity, numBeliefMap, False)46        self.m6_2 = DopeNetwork.create_stage(128 + numBeliefMap + numAffinity, numBeliefMap, False)47        # print('---Affinity----------------------------------------------')48        # _1 are the affinity map stages49        self.m1_1 = DopeNetwork.create_stage(128, numAffinity, True)50        self.m2_1 = DopeNetwork.create_stage(128 + numBeliefMap + numAffinity, numAffinity, False)51        self.m3_1 = DopeNetwork.create_stage(128 + numBeliefMap + numAffinity, numAffinity, False)52        self.m4_1 = DopeNetwork.create_stage(128 + numBeliefMap + numAffinity, numAffinity, False)53        self.m5_1 = DopeNetwork.create_stage(128 + numBeliefMap + numAffinity, numAffinity, False)54        self.m6_1 = DopeNetwork.create_stage(128 + numBeliefMap + numAffinity, numAffinity, False)55    def forward(self, x):56        '''Runs inference on the neural network'''57        out1 = self.vgg(x)58        out1_2 = self.m1_2(out1)59        out1_1 = self.m1_1(out1)60        out2 = torch.cat([out1_2, out1_1, out1], 1)61        out2_2 = self.m2_2(out2)62        out2_1 = self.m2_1(out2)63        out3 = torch.cat([out2_2, out2_1, out1], 1)64        out3_2 = self.m3_2(out3)65        out3_1 = self.m3_1(out3)66        out4 = torch.cat([out3_2, out3_1, out1], 1)67        out4_2 = self.m4_2(out4)68        out4_1 = self.m4_1(out4)69        out5 = torch.cat([out4_2, out4_1, out1], 1)70        out5_2 = self.m5_2(out5)71        out5_1 = self.m5_1(out5)72        out6 = torch.cat([out5_2, out5_1, out1], 1)73        out6_2 = self.m6_2(out6)74        out6_1 = self.m6_1(out6)75        return torch.cat([out6_2, out6_1], 1)76    @staticmethod77    def create_stage(in_channels, out_channels, first=False):78        '''Create the neural network layers for a single stage.'''79        model = nn.Sequential()80        mid_channels = 12881        if first:82            padding = 183            kernel = 384            count = 685            final_channels = 51286        else:87            padding = 388            kernel = 789            count = 1090            final_channels = mid_channels91        # First convolution...

stages.py

Source:stages.py

...4    Carrega a stage cliente5    :param connection: conexÃ£o com o banco de dados6    :return: None7    """8    dwt.create_stage(9        conn_input=connection,10        conn_output=connection,11        schema_in="public",12        table="CLIENTE",13        stg_name="stg_cliente",14        tbl_exists="replace"15    )16def run_stg_endereco(connection):17    """18    Carrega a stage endereco19    :param connection: conexÃ£o com o banco de dados20    :return: None21    """22    dwt.create_stage(23        conn_input=connection,24        conn_output=connection,25        schema_in="public",26        table="ENDERECO",27        stg_name="stg_endereco",28        tbl_exists="replace"29    )30def run_stg_forma_pagamento(connection):31    """32    Carrega a stage forma_pagamento33    :param connection: conexÃ£o com o banco de dados34    :return: None35    """36    dwt.create_stage(37        conn_input=connection,38        conn_output=connection,39        schema_in="public",40        table="FORMA_PAGAMENTO",41        stg_name="stg_forma_pagamento",42        tbl_exists="replace"43    )44def run_stg_funcionario(connection):45    """46    Carrega a stage funcionario47    :param connection: conexÃ£o com o banco de dados48    :return: None49    """50    dwt.create_stage(51        conn_input=connection,52        conn_output=connection,53        schema_in="public",54        table="FUNCIONARIO",55        stg_name="stg_funcionario",56        tbl_exists="replace"57    )58def run_stg_item_venda(connection):59    """60    Carrega a stage item_venda61    :param connection: conexÃ£o com o banco de dados62    :return: None63    """64    dwt.create_stage(65        conn_input=connection,66        conn_output=connection,67        schema_in="public",68        table="ITEM_VENDA",69        stg_name="stg_item_venda",70        tbl_exists="replace"71    )72def run_stg_venda(connection):73    """74    Carrega a stage venda75    :param connection: conexÃ£o com o banco de dados76    :return: None77    """78    dwt.create_stage(79        conn_input=connection,80        conn_output=connection,81        schema_in="public",82        table="VENDA",83        stg_name="stg_venda",84        tbl_exists="replace"...

patient_stage.py

Source:patient_stage.py

...8    for r in Registry.objects.all():9        if r.has_feature(RegistryFeatures.STAGES):10            init_registry_stages_and_rules(r)11def init_registry_stages_and_rules(registry):12        informed_consent = create_stage(registry, 'Informed Consent', None)13        eligibility = create_stage(registry, 'Eligibility', informed_consent)14        pre_screening = create_stage(registry, 'Pre-screening', eligibility)15        screening = create_stage(registry, 'Screening', pre_screening)16        run_in = create_stage(registry, 'Run-in', screening)17        trial = create_stage(registry, 'Trial', run_in)18        _ = create_stage(registry, 'Follow-up', trial)19        if informed_consent and eligibility:20            create_rule(registry, None, 'registered', informed_consent, 1)21            create_rule(registry, informed_consent, 'consented', eligibility, 1)22def create_stage(registry, name, previous_stage):23    stage, created = models.PatientStage.objects.get_or_create(name=name, registry=registry)24    if created and previous_stage:25        stage.allowed_prev_stages.add(previous_stage)26        previous_stage.allowed_next_stages.add(stage)27    return stage if created else None28def create_rule(registry, from_stage, rule, to_stage, order):29    models.PatientStageRule.objects.get_or_create(30        registry=registry,31        from_stage=from_stage, 32        condition=rule, 33        to_stage=to_stage,34        order=order...

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