Add prepare_data method to generate and save protocol data on disk
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The goal of this method is to generate the data needed by the task and save it on disk for future uses, for example by the `setup` method. The objective is to avoid systematically recreating data on each process at the beginning of a training
hbredin
reviewed
Oct 13, 2023
Comment on lines
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| data_dict = pickle.load(data_file) | ||
| self.metadata = data_dict["metadata"] | ||
| self.audios = data_dict["audios"] | ||
| self.audio_infos= data_dict["audio_infos"] | ||
| self.audio_encodings = data_dict["audio_encodings"] | ||
| self.annotated_duration = data_dict["annotated_duration"] | ||
| self.annotated_regions = data_dict["annotated_regions"] | ||
| self.annotated_classes = data_dict["annotated_classes"] | ||
| self.annotations = data_dict["annotations"] | ||
| self.metadata_unique_values = data_dict["metadata_unique_values"] | ||
| if isinstance(self.protocol, SegmentationProtocol): | ||
| self.classes = data_dict["classes"] | ||
| if self.has_validation: | ||
| self.validation_chunks = data_dict["validation_chunks"] |
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All of this can probably be replaced by:
for key, value in data_dict.items():
setattr(self, key) = value
Now all the segmentations tasks in `pyannote` inherit the `SegmentationTask` (previously `SegmentationTaskMixin`), which inherits the `Task` class. This commit also adds a `prepared_data` attribute to the `Task` class. That attribute is a dict which contains all the prepared data by the `prepare_data` method.
…note-audio into feat/data_preparation
One for the test of the `MultiLabelSegmentation` task, and the other for the test of the `SupervisedRepresentationLearningWithArcFace` task.
This eliminates the need to reload pickle data in setup when in the main process
…note-audio into feat/data_preparation
This issue occured when a list of classes was specified during `MultiLabelSegmentation` instanciation.
* use npz archive instead pickle to save task data * improve code readability * improve(task): update numpy array dtypes In order to use types whose size better machtes the contents of the arrays * remove `end` entry from `annotated_regions` numpy array This entry was redundant with the start and duration entries, since `end` = `start` + `duration`. * fix: allow data preparation to be finished when task has no validation * improve: clear data lists after assignation to `self.prepared_data` This is to avoid data redundancy in the `prepare_data` method --------- Co-authored-by: clement-pages <clement.pages@irit.fr>
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Looks like you closed this PR? |
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This is not me... I think the PR was closed because I have renamed the branch |
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The goal of this pull request is to reduce the time needed to setup a task in
pyannoteby implementingprepare_datamethod.prepare_datagenerates the data needed by the task and save it on disk for future uses, for example by thesetupmethod. The objective is to avoid systematically recreating data on each process at the beginning of a training.This PR also proposes to reorganize the segmentation tasks hierarchy. Now, the
SegmentationTask(previouslySegmentationTaskMixin) inherits theTaskclass. All the segmentation tasks inherit only from theSegmentationTask(instead ofTaskandSegmentationTaskMixin)