Best Python code snippet using localstack_python
load_model.py
Source: load_model.py
...50 help='Number of Instances to create',51 type=int,52 default=225053 )54 def get_model_template(self, template_name):55 result = []56 subconfig = self.model_config.get(template_name, [])57 for otype_dict in subconfig:58 modname = otype_dict.keys()[0]59 for obj_id, obj_attrs in otype_dict.values()[0].iteritems():60 obj_attrs['id'] = obj_id61 result.append((modname, dict(obj_attrs)))62 return result63 def talesEvalAttrs(self, obj_attrs, **kwargs):64 for attr in obj_attrs:65 if isinstance(obj_attrs[attr], list):66 obj_attrs[attr] = [talesEvalStr(x, self, extra=kwargs) for x in obj_attrs[attr]]67 else:68 obj_attrs[attr] = talesEvalStr(obj_attrs[attr], self, extra=kwargs)69 def run(self):70 with open('model.yaml', 'r') as f:71 self.model_config = yaml.load(f)72 self.connect()73 objmaps = []74 for modname, obj_attrs in self.get_model_template("Global"):75 objmaps.append(ObjectMap(modname=modname, data=obj_attrs))76 for controller_num in range(1, self.options.controllers + 1):77 for modname, obj_attrs in self.get_model_template("Controller"):78 self.talesEvalAttrs(79 obj_attrs,80 num=controller_num,81 device_name=self.options.device82 )83 objmaps.append(ObjectMap(modname=modname, data=obj_attrs))84 for compute_num in range(1, self.options.computes + 1):85 for modname, obj_attrs in self.get_model_template("Compute"):86 self.talesEvalAttrs(87 obj_attrs,88 num=compute_num,89 device_name=self.options.device90 )91 objmaps.append(ObjectMap(modname=modname, data=obj_attrs))92 for tenant_num in range(3, self.options.tenants + 3):93 for modname, obj_attrs in self.get_model_template("Tenant"):94 self.talesEvalAttrs(95 obj_attrs,96 num=tenant_num,97 device_name=self.options.device98 )99 objmaps.append(ObjectMap(modname=modname, data=obj_attrs))100 compute_nums = range(1, self.options.computes + 1)101 tenant_nums = range(3, self.options.tenants + 3)102 for instance_num in range(1, self.options.instances + 1):103 for modname, obj_attrs in self.get_model_template("Instance"):104 tenant_num = tenant_nums[instance_num % self.options.tenants]105 compute_num = compute_nums[instance_num % self.options.computes]106 self.talesEvalAttrs(107 obj_attrs,108 num=instance_num,109 device_name=self.options.device,110 tenant_num=tenant_num,111 compute_num=compute_num112 )113 objmaps.append(ObjectMap(modname=modname, data=obj_attrs))114 device = self.dmd.Devices.OpenStack.Infrastructure.findDevice(self.options.device)115 if not device:116 print "Creating OpenStackInfrastructure device %s" % self.options.device117 device = self.dmd.Devices.OpenStack.Infrastructure.createInstance(self.options.device)...
csharp_sca.py
Source: csharp_sca.py
...21 if not isExist:22 os.makedirs(path)23 for table in self.__tables:24 file = open(f"{path}/{table['name']}.cs", "w")25 file.write(get_model_template(table, self.__models_namespace))26 file.close()27 def create_interfaces(self):28 path = f"{self.__output_path}/Repositories"29 isExist = os.path.exists(path)30 if not isExist:31 os.makedirs(path)32 for table in self.__tables:33 file = open(f"{path}/I{table['name']}Repository.cs", "w")34 file.write(get_irepository_template(table, self.__models_namespace, self.__repositories_namespace))35 file.close()36 def create_sqlserver_repository(self, connection_string):37 path = f"{self.__output_path}/Repositories"38 isExist = os.path.exists(path)39 if not isExist:...
test_byom.py
Source: test_byom.py
...5from functools import lru_cache6import nbox7from nbox import utils8@lru_cache()9def get_model_template(spec: str):10 if spec == "I-I":11 # this is an image image model, we hard code to 3 inputs12 class NHeadModel(torch.nn.Module):13 def __init__(self):14 super().__init__()15 # input_size = (10, 10, 3)16 self.a = torch.nn.Linear(300, 3)17 # input_size = (5, 5, 3)18 self.b = torch.nn.Linear(75, 3)19 def forward(self, a, b):20 return self.a(a.reshape(a.shape[0], -1)) + self.b(b.reshape(b.shape[0], -1))21 # load the model22 model = nbox.Model(NHeadModel(), category={"a": "image", "b": "image"})23 return model, {"a": [1, 3, 10, 10], "b": [1, 3, 5, 5]}24 elif spec == "I-T":25 raise NotImplementedError("TODO")26class PytorchModelLoader(unittest.TestCase):27 def test_image_image_model(self):28 model, templates = get_model_template("I-I")29 # define parser30 parser = nbox.ImageParser(31 post_proc_fn=None,32 templates=templates,33 )34 image = os.path.join(utils.folder(__file__), "assets/cat.jpg")35 # simple fp list -> should fail36 with self.assertRaises(Exception):37 out = parser([image, image])38 # simple dict with 1 input39 out = parser({"a": image, "b": image})40 self.assertEqual(41 {k: v.shape for k, v in out.items()},42 {...
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