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from s3prl.corpus.hear import dcase_2016_task2 |
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from s3prl.nn.hear import HearFullyConnectedPrediction |
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from s3prl.task.event_prediction import EventPredictionTask |
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from s3prl.util.configuration import default_cfg, field |
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from .timestamp import HearTimestamp |
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class Dcase2016Task2(HearTimestamp): |
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@default_cfg( |
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**HearTimestamp.setup.default_except( |
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corpus=dict( |
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CLS=field( |
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dcase_2016_task2, |
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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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), |
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downstream=dict( |
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CLS=field( |
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HearFullyConnectedPrediction, |
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"\nThe downstream model class for each task. You can add the **kwargs right below this CLS key", |
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str, |
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), |
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output_size=11, |
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hidden_layers=2, |
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), |
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task=dict( |
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CLS=field( |
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EventPredictionTask, |
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"\nThe task class defining what to do for each train/valid/test step in the train/valid/test dataloader loop" |
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"\nYou can add the **kwargs right below this CLS key", |
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str, |
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), |
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prediction_type="multilabel", |
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scores=["event_onset_200ms_fms", "segment_1s_er"], |
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postprocessing_grid={ |
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"median_filter_ms": [250], |
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"min_duration": [125, 250], |
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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 setup(cls, **cfg): |
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super().setup(**cfg) |
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@default_cfg( |
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**HearTimestamp.train.default_except( |
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optimizer=dict( |
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CLS="torch.optim.Adam", |
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lr=1.0e-3, |
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), |
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trainer=dict( |
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total_steps=15000, |
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log_step=100, |
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eval_step=500, |
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save_step=500, |
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gradient_clipping=1.0, |
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gradient_accumulate_steps=1, |
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valid_metric="event_onset_200ms_fms", |
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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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""" |
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Train the setup problem with the train/valid datasets & samplers and the task object |
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""" |
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super().train(**cfg) |
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@default_cfg(**HearTimestamp.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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**HearTimestamp.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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