Removed Neural Network

docs/source/api_reference/api_client.rst

@@ -18,4 +18,3 @@ Contents
     api_subset
     api_image
     api_team
-    api_neural_network

src/superannotate/lib/app/interface/sdk_interface.py

@@ -55,7 +55,6 @@
 from lib.core.enums import ProjectType
 from lib.core.enums import ClassTypeEnum
 from lib.core.exceptions import AppException
-from lib.core.types import MLModel
 from lib.core.types import PriorityScoreEntity
 from lib.core.types import Project
 from lib.core.pydantic_v1 import ValidationError
@@ -1923,27 +1922,6 @@ def upload_image_annotations(
         if response.errors and not response.errors == constants.INVALID_JSON_MESSAGE:
             raise AppException(response.errors)
 
-    def download_model(self, model: MLModel, output_dir: Union[str, Path]):
-        """Downloads the neural network and related files
-        which are the <model_name>.pth/pkl. <model_name>.json, <model_name>.yaml, classes_mapper.json
-
-        :param model: the model that needs to be downloaded
-        :type  model: dict
-
-        :param output_dir: the directory in which the files will be saved
-        :type output_dir: str
-
-        :return: the metadata of the model
-        :rtype: dict
-        """
-        res = self.controller.models.download(
-            model_data=model.dict(), download_path=output_dir
-        )
-        if res.errors:
-            logger.error("\n".join([str(error) for error in res.errors]))
-        else:
-            return BaseSerializer(res.data).serialize()
-
     def consensus(
         self,
         project: NotEmptyStr,
@@ -1983,48 +1961,6 @@ def consensus(
             raise AppException(response.errors)
         return response.data
 
-    def run_prediction(
-        self,
-        project: Union[NotEmptyStr, dict],
-        images_list: List[NotEmptyStr],
-        model: Union[NotEmptyStr, dict],
-    ):
-        """This function runs smart prediction on given list of images from a given project
-            using the neural network of your choice
-
-        :param project: the project in which the target images are uploaded.
-        :type project: str or dict
-
-        :param images_list: the list of image names on which smart prediction has to be run
-        :type images_list: list of str
-
-        :param model: the name of the model that should be used for running smart prediction
-        :type model: str or dict
-
-        :return: tuple of two lists, list of images on which the prediction has succeeded and failed respectively
-        :rtype: tuple
-        """
-        project_name = None
-        folder_name = None
-        if isinstance(project, dict):
-            project_name = project["name"]
-        if isinstance(project, str):
-            project_name, folder_name = extract_project_folder(project)
-
-        model_name = model
-        if isinstance(model, dict):
-            model_name = model["name"]
-        project, folder = self.controller.get_project_folder(project_name, folder_name)
-        response = self.controller.models.run_prediction(
-            project=project,
-            folder=folder,
-            items_list=images_list,
-            model_name=model_name,
-        )
-        if response.errors:
-            raise AppException(response.errors)
-        return response.data
-
     def upload_image_to_project(
         self,
         project: NotEmptyStr,
@@ -2073,49 +2009,6 @@ def upload_image_to_project(
         if response.errors:
             raise AppException(response.errors)
 
-    def search_models(
-        self,
-        name: Optional[NotEmptyStr] = None,
-        type_: Optional[NotEmptyStr] = None,  # noqa
-        project_id: Optional[int] = None,
-        task: Optional[NotEmptyStr] = None,
-        include_global: Optional[bool] = True,
-    ):
-        r"""Search for ML models.
-
-        :param name: search string
-        :type name: str
-
-        :param type\_: ml model type string
-        :type type\_: str
-
-        :param project_id: project id
-        :type project_id: int
-
-        :param task: training task
-        :type task: str
-
-        :param include_global: include global ml models
-        :type include_global: bool
-
-        :return: ml model metadata
-        :rtype: list of dicts
-        """
-        condition = EmptyCondition()
-        if name:
-            condition &= Condition("name", name, EQ)
-        if type_:
-            condition &= Condition("type", type_, EQ)
-        if project_id:
-            condition &= Condition("project_id", project_id, EQ)
-        if task:
-            condition &= Condition("task", task, EQ)
-        if include_global:
-            condition &= Condition("include_global", include_global, EQ)
-
-        res = self.controller.models.list(condition)
-        return res.data
-
     def upload_images_to_project(
         self,
         project: NotEmptyStr,

src/superannotate/lib/core/entities/__init__.py

@@ -12,7 +12,6 @@
 from lib.core.entities.items import VideoEntity
 from lib.core.entities.project import AttachmentEntity
 from lib.core.entities.project import ContributorEntity
-from lib.core.entities.project import MLModelEntity
 from lib.core.entities.project import ProjectEntity
 from lib.core.entities.project import SettingEntity
 from lib.core.entities.project import TeamEntity
@@ -47,6 +46,5 @@
     "AnnotationClassEntity",
     "TeamEntity",
     "UserEntity",
-    "MLModelEntity",
     "IntegrationEntity",
 ]

src/superannotate/lib/core/entities/project.py

@@ -142,29 +142,6 @@ def __eq__(self, other):
         return self.id == other.id
 
 
-class MLModelEntity(TimedBaseModel):
-    id: Optional[int]
-    team_id: Optional[int]
-    name: Optional[str]
-    path: Optional[str]
-    config_path: Optional[str]
-    model_type: Optional[int]
-    description: Optional[str]
-    output_path: Optional[str]
-    task: Optional[str]
-    base_model_id: Optional[int]
-    image_count: Optional[int]
-    training_status: Optional[int]
-    test_folder_ids: Optional[List[int]]
-    train_folder_ids: Optional[List[int]]
-    is_trainable: Optional[bool]
-    is_global: Optional[bool]
-    hyper_parameters: Optional[dict]
-
-    class Config:
-        extra = Extra.ignore
-
-
 class UserEntity(BaseModel):
     id: Optional[str]
     first_name: Optional[str]

src/superannotate/lib/core/service_types.py

@@ -52,30 +52,6 @@ def __init__(self, **data):
         super().__init__(**data)
 
 
-class DownloadMLModelAuthData(BaseModel):
-    access_key: str
-    secret_key: str
-    session_token: str
-    region: str
-    bucket: str
-    paths: List[str]
-
-    class Config:
-        extra = Extra.allow
-        fields = {
-            "access_key": "accessKeyId",
-            "secret_key": "secretAccessKey",
-            "session_token": "sessionToken",
-            "region": "region",
-        }
-
-    def __init__(self, **data):
-        credentials = data["tokens"]
-        data.update(credentials)
-        del data["tokens"]
-        super().__init__(**data)
-
-
 class UploadAnnotations(BaseModel):
     class Resource(BaseModel):
         classes: List[str] = Field([], alias="class")
@@ -202,10 +178,6 @@ class SubsetResponse(ServiceResponse):
     res_data: entities.SubSetEntity = None
 
 
-class DownloadMLModelAuthDataResponse(ServiceResponse):
-    res_data: DownloadMLModelAuthData = None
-
-
 class UploadAnnotationsResponse(ServiceResponse):
     res_data: Optional[UploadAnnotations] = None
 

src/superannotate/lib/core/serviceproviders.py

@@ -10,12 +10,10 @@
 from lib.core.conditions import Condition
 from lib.core.reporter import Reporter
 from lib.core.service_types import AnnotationClassListResponse
-from lib.core.service_types import DownloadMLModelAuthDataResponse
 from lib.core.service_types import FolderListResponse
 from lib.core.service_types import FolderResponse
 from lib.core.service_types import IntegrationListResponse
 from lib.core.service_types import ItemListResponse
-from lib.core.service_types import ModelListResponse
 from lib.core.service_types import ProjectListResponse
 from lib.core.service_types import ProjectResponse
 from lib.core.service_types import ServiceResponse
@@ -469,20 +467,6 @@ def add_items(
         raise NotImplementedError
 
 
-class BaseModelsService(SuperannotateServiceProvider):
-    @abstractmethod
-    def delete(self, model_id: int) -> ServiceResponse:
-        raise NotImplementedError
-
-    @abstractmethod
-    def start_training(self, model_id: int) -> ServiceResponse:
-        raise NotImplementedError
-
-    @abstractmethod
-    def list(self, condition: Condition = None) -> ModelListResponse:
-        raise NotImplementedError
-
-
 class BaseIntegrationService(SuperannotateServiceProvider):
     @abstractmethod
     def list(self) -> IntegrationListResponse:
@@ -507,7 +491,6 @@ class BaseServiceProvider:
     custom_fields: BaseCustomFieldService
     annotation_classes: BaseAnnotationClassService
     subsets: BaseSubsetService
-    models: BaseModelsService
     integrations: BaseIntegrationService
 
     @abstractmethod
@@ -579,28 +562,12 @@ def prepare_export(
     def get_exports(self, project: entities.ProjectEntity) -> ServiceResponse:
         raise NotImplementedError
 
-    @abstractmethod
-    def get_model_metrics(self, model_id: int) -> ServiceResponse:
-        raise NotImplementedError
-
     @abstractmethod
     def get_export(
         self, project: entities.ProjectEntity, export_id: int
     ) -> ServiceResponse:
         raise NotImplementedError
 
-    @abstractmethod
-    def get_ml_model_download_tokens(
-        self, model_id: int
-    ) -> DownloadMLModelAuthDataResponse:
-        raise NotImplementedError
-
-    @abstractmethod
-    def run_prediction(
-        self, project: entities.ProjectEntity, ml_model_id: int, image_ids: list
-    ) -> ServiceResponse:
-        raise NotImplementedError
-
     @abstractmethod
     def get_project_images_count(
         self, project: entities.ProjectEntity

src/superannotate/lib/core/types.py

@@ -14,17 +14,6 @@ class Config:
         extra = Extra.allow
 
 
-class MLModel(BaseModel):
-    name: NotEmptyStr
-    id: Optional[int]
-    path: NotEmptyStr
-    config_path: NotEmptyStr
-    team_id: Optional[int]
-
-    class Config:
-        extra = Extra.allow
-
-
 class PriorityScoreEntity(BaseModel):
     name: NotEmptyStr
     priority: float