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