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| Original file line number | Diff line number | Diff line change |
|---|---|---|
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| # Copyright (C) 2024 Intel Corporation | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, | ||
| # software distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions | ||
| # and limitations under the License. | ||
|
|
||
| from collections import UserList | ||
| from typing import Any, Dict, List, Optional, Sequence | ||
|
|
||
| from geti_sdk.data_models.label import Label, ScoredLabel | ||
| from geti_sdk.data_models.project import Project | ||
| from geti_sdk.utils.serialization_helpers import deserialize_dictionary | ||
|
|
||
|
|
||
| class LabelList(UserList): | ||
| """ | ||
| A list containing labels for an Intel® Geti™ inference model. | ||
| """ | ||
|
|
||
| def __init__(self, data: Optional[Sequence[Label]] = None): | ||
| self.data: List[Label] = [] | ||
| if data is not None: | ||
| super().__init__(list(data)) | ||
|
|
||
| self._id_mapping: Dict[str, Label] = {} | ||
| self._name_mapping: Dict[str, Label] = {} | ||
| self._generate_indices() | ||
| self._empty_label = next((x for x in self.data if x.is_empty), None) | ||
|
|
||
| @property | ||
| def has_empty_label(self) -> bool: | ||
| """ | ||
| Return True if the list of Labels contains an empty label | ||
| """ | ||
| return self._empty_label is not None | ||
|
|
||
| def _generate_indices(self): | ||
| """ | ||
| Map names and ID's to Label objects to enable quick label retrieval | ||
| """ | ||
| self._id_mapping = {x.id: x for x in self.data} | ||
| self._name_mapping = {x.name: x for x in self.data} | ||
|
|
||
| def get_by_id(self, id: str) -> Label: | ||
| """ | ||
| Return the Label object with ID corresponding to `id` | ||
| """ | ||
| label = self._id_mapping.get(id, None) | ||
| if label is None: | ||
| raise KeyError(f"Label with id `{id}` was not found in the LabelList") | ||
| return label | ||
|
|
||
| def get_by_name(self, name: str) -> Label: | ||
| """ | ||
| Return the Label object named `name` | ||
| """ | ||
| label = self._name_mapping.get(name, None) | ||
| if label is None: | ||
| raise KeyError(f"Label named `{name}` was not found in the LabelList") | ||
| return label | ||
|
|
||
| @classmethod | ||
| def from_json(cls, input_json: List[Dict[str, Any]]) -> "LabelList": | ||
| """ | ||
| Create a LabelList object from json input. Input should be formatted as a list | ||
| of dictionaries, each representing a single Label | ||
| """ | ||
| label_list: List[Label] = [] | ||
| for item in input_json: | ||
| label_list.append(deserialize_dictionary(item, Label)) | ||
| return cls(label_list) | ||
|
|
||
| def create_scored_label(self, id_or_name: str, score: float) -> ScoredLabel: | ||
| """ | ||
| Return a ScoredLabel object corresponding to the label identified by | ||
| `id_or_name`, and with an assigned probability score of `score` | ||
| :param id_or_name: ID or name of the Label to assign | ||
| :param score: probability score of the label | ||
| """ | ||
| try: | ||
| label = self.get_by_id(id_or_name) | ||
| except KeyError: | ||
| label = self.get_by_name(id_or_name) | ||
| return ScoredLabel.from_label(label, probability=score) | ||
|
|
||
| def get_empty_label(self) -> Optional[Label]: | ||
| """ | ||
| Return the empty label, if the LabelList contains one. If not, return None | ||
| """ | ||
| return self._empty_label | ||
|
|
||
| def get_non_empty_labels(self) -> "LabelList": | ||
| """ | ||
| Return all non-empty labels | ||
| """ | ||
| return LabelList([x for x in self.data if not x.is_empty]) | ||
|
|
||
| @classmethod | ||
| def from_project(cls, project: Project, task_index: int = 0) -> "LabelList": | ||
| """ | ||
| Create a LabelList object for the 'project', corresponding to the trainable | ||
| task addressed by `task_index` | ||
| :param project: Project for which to get the list of labels | ||
| :param task_index: Index of the task for which to get the list of labels. | ||
| Defaults to 0, i.e. the first trainable task in the project | ||
| """ | ||
| task = project.pipeline.trainable_tasks[task_index] | ||
| return cls(task.labels) | ||
|
|
||
| def sort_by_ids(self, label_ids: List[str]): | ||
| """ | ||
| Sort the labels in the LabelList by their ID, according to the order defined | ||
| in `label_ids` | ||
| """ | ||
| new_data: List[Label] = [] | ||
| for label_id in label_ids: | ||
| new_data.append(self.get_by_id(label_id)) | ||
| self.data = new_data |
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