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import logging |
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from s3prl.corpus.hear import hear_scene_kfolds |
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from s3prl.util.configuration import default_cfg, field |
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from .scene import HearScene |
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logger = logging.getLogger(__name__) |
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NUM_FOLDS = 5 |
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class Stroke(HearScene): |
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@default_cfg( |
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**HearScene.setup.default_except( |
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corpus=dict( |
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CLS=field( |
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hear_scene_kfolds, |
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"\nThe corpus class. You can add the **kwargs right below this CLS key", |
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str, |
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), |
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dataset_root=field( |
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"???", |
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"The root path of the corpus", |
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str, |
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), |
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test_fold=field("???", "The testing fold id. Options: [0, 1, 2, 3, 4]"), |
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num_folds=NUM_FOLDS, |
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), |
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train_sampler=dict( |
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batch_size=32, |
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), |
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task=dict( |
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prediction_type="multiclass", |
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scores=["top1_acc", "d_prime", "aucroc", "mAP"], |
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), |
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) |
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) |
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@classmethod |
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def setup(cls, **cfg): |
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super().setup(**cfg) |
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@default_cfg( |
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**HearScene.train.default_except( |
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trainer=dict( |
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valid_metric="top1_acc", |
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valid_higher_better=True, |
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) |
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) |
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) |
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@classmethod |
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def train(cls, **cfg): |
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super().train(**cfg) |
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@default_cfg(**HearScene.inference.default_cfg) |
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@classmethod |
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def inference(cls, **cfg): |
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super().inference(**cfg) |
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@default_cfg( |
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**HearScene.run.default_except( |
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stages=["setup", "train", "inference"], |
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start_stage="setup", |
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final_stage="inference", |
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setup=setup.default_cfg.deselect("workspace", "resume"), |
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train=train.default_cfg.deselect("workspace", "resume"), |
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inference=inference.default_cfg.deselect("workspace", "resume"), |
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) |
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) |
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@classmethod |
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def run(cls, **cfg): |
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super().run(**cfg) |
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@default_cfg( |
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num_fold=field(NUM_FOLDS, "The number of folds to run cross validation", int), |
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**run.default_except( |
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workspace=field( |
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"???", |
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"The root workspace for all folds.\n" |
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"Each fold will use a 'fold_{id}' sub-workspace under this root workspace", |
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), |
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setup=dict( |
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corpus=dict( |
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test_fold=field( |
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"TBD", "This will be auto-set by 'run_cross_validation'" |
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) |
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) |
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), |
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), |
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) |
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@classmethod |
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def cross_validation(cls, **cfg): |
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super().cross_validation(**cfg) |
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