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from omegaconf import MISSING
from .hear_esc50 import HearESC50
STROKE_NUM_FOLDS = 5
__all__ = ["HearStroke"]
class HearStroke(HearESC50):
def default_config(self) -> dict:
return dict(
start=0,
stop=None,
target_dir=MISSING,
cache_dir=None,
remove_all_cache=False,
prepare_data=dict(
dataset_root=MISSING,
test_fold=MISSING,
num_folds=STROKE_NUM_FOLDS,
),
build_batch_sampler=dict(
train=dict(
batch_size=32,
shuffle=True,
),
valid=dict(
batch_size=1,
),
test=dict(
batch_size=1,
),
),
build_upstream=dict(
name=MISSING,
),
build_featurizer=dict(
layer_selections=None,
normalize=False,
),
build_downstream=dict(
hidden_layers=2,
pooling_type="MeanPooling",
),
build_model=dict(
upstream_trainable=False,
),
build_task=dict(
prediction_type="multiclass",
scores=["top1_acc", "d_prime", "aucroc", "mAP"],
),
build_optimizer=dict(
name="Adam",
conf=dict(
lr=1.0e-3,
),
),
build_scheduler=dict(
name="ExponentialLR",
gamma=0.9,
),
save_model=dict(),
save_task=dict(),
train=dict(
total_steps=150000,
log_step=100,
eval_step=1000,
save_step=100,
gradient_clipping=1.0,
gradient_accumulate=1,
valid_metric="top1_acc",
valid_higher_better=True,
auto_resume=True,
resume_ckpt_dir=None,
),
evaluate=dict(),
)
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