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config_updates.py
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config_updates.py
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from sacred.config_helpers import DynamicIngredient, CMD
def add_configs(ex):
'''
This functions add generic configuration for the experiments, such as mix-up, architectures, etc...
@param ex: Ba3l Experiment
@return:
'''
@ex.named_config
def nomixup():
'Don\'t apply mix-up (spectrogram level).'
use_mixup = False
mixup_alpha = 0.3
@ex.named_config
def mixup():
' Apply mix-up (spectrogram level).'
use_mixup = True
mixup_alpha = 0.3
@ex.named_config
def mini_train():
'limit training/validation to 5 batches for debbuging.'
trainer = dict(limit_train_batches=5, limit_val_batches=5)
@ex.named_config
def passt():
'use PaSST model'
models = {
"net": DynamicIngredient("models.passt.model_ing")
}
@ex.named_config
def passt_s_20sec():
'use PaSST model pretrained on Audioset (with SWA) ap=476; time encodings for up to 20 seconds'
# python ex_audioset.py evaluate_only with passt_s_ap476
models = {
"net": DynamicIngredient("models.passt.model_ing", arch="passt_s_f128_20sec_p16_s10_ap474", fstride=10,
tstride=10, input_tdim=2000)
}
basedataset = dict(clip_length=20)
@ex.named_config
def passt_s_30sec():
'use PaSST model pretrained on Audioset (with SWA) ap=476; time encodings for up to 30 seconds'
# python ex_audioset.py evaluate_only with passt_s_ap476
models = {
"net": DynamicIngredient("models.passt.model_ing", arch="passt_s_f128_30sec_p16_s10_ap473", fstride=10,
tstride=10, input_tdim=3000)
}
basedataset = dict(clip_length=20)
@ex.named_config
def passt_s_ap476():
'use PaSST model pretrained on Audioset (with SWA) ap=476'
# python ex_audioset.py evaluate_only with passt_s_ap476
models = {
"net": DynamicIngredient("models.passt.model_ing", arch="passt_s_swa_p16_128_ap476", fstride=10,
tstride=10)
}
@ex.named_config
def passt_s_ap4763():
'use PaSST model pretrained on Audioset (with SWA) ap=4763'
# test with: python ex_audioset.py evaluate_only with passt_s_ap4763
models = {
"net": DynamicIngredient("models.passt.model_ing", arch="passt_s_swa_p16_128_ap4763", fstride=10,
tstride=10)
}
@ex.named_config
def passt_s_ap472():
'use PaSST model pretrained on Audioset (no SWA) ap=472'
# test with: python ex_audioset.py evaluate_only with passt_s_ap472
models = {
"net": DynamicIngredient("models.passt.model_ing", arch="passt_s_p16_128_ap472", fstride=10,
tstride=10)
}
@ex.named_config
def passt_s_p16_s16_128_ap468():
'use PaSST model pretrained on Audioset (no SWA) ap=468 NO overlap'
# test with: python ex_audioset.py evaluate_only with passt_s_p16_s16_128_ap468
models = {
"net": DynamicIngredient("models.passt.model_ing", arch="passt_s_p16_s16_128_ap468", fstride=16,
tstride=16)
}
@ex.named_config
def passt_s_swa_p16_s16_128_ap473():
'use PaSST model pretrained on Audioset (SWA) ap=473 NO overlap'
# test with: python ex_audioset.py evaluate_only with passt_s_swa_p16_s16_128_ap473
models = {
"net": DynamicIngredient("models.passt.model_ing", arch="passt_s_swa_p16_s16_128_ap473", fstride=16,
tstride=16)
}
@ex.named_config
def passt_s_swa_p16_s14_128_ap471():
'use PaSST model pretrained on Audioset stride=14 (SWA) ap=471 '
# test with: python ex_audioset.py evaluate_only with passt_s_swa_p16_s14_128_ap471
models = {
"net": DynamicIngredient("models.passt.model_ing", arch="passt_s_swa_p16_s14_128_ap471", fstride=14,
tstride=14)
}
@ex.named_config
def passt_s_p16_s14_128_ap469():
'use PaSST model pretrained on Audioset stride=14 (No SWA) ap=469 '
# test with: python ex_audioset.py evaluate_only with passt_s_p16_s14_128_ap469
models = {
"net": DynamicIngredient("models.passt.model_ing", arch="passt_s_p16_s14_128_ap469", fstride=14,
tstride=14)
}
@ex.named_config
def passt_s_swa_p16_s12_128_ap473():
'use PaSST model pretrained on Audioset stride=12 (SWA) ap=473 '
# test with: python ex_audioset.py evaluate_only with passt_s_swa_p16_s12_128_ap473
models = {
"net": DynamicIngredient("models.passt.model_ing", arch="passt_s_swa_p16_s12_128_ap473", fstride=12,
tstride=12)
}
@ex.named_config
def passt_s_p16_s12_128_ap470():
'use PaSST model pretrained on Audioset stride=12 (No SWA) ap=4670 '
# test with: python ex_audioset.py evaluate_only with passt_s_p16_s12_128_ap470
models = {
"net": DynamicIngredient("models.passt.model_ing", arch="passt_s_p16_s12_128_ap470", fstride=12,
tstride=12)
}
@ex.named_config
def ensemble_s10():
'use ensemble of PaSST models pretrained on Audioset with S10 mAP=.4864'
# test with: python ex_audioset.py evaluate_only with trainer.precision=16 ensemble_s10
models = {
"net": DynamicIngredient("models.passt.model_ing", arch="ensemble_s10", fstride=None,
tstride=None, instance_cmd="get_ensemble_model",
# don't call get_model but rather get_ensemble_model
arch_list=[
("passt_s_swa_p16_128_ap476", 10, 10),
("passt_s_swa_p16_128_ap4761", 10, 10),
("passt_s_p16_128_ap472", 10, 10),
]
)
}
@ex.named_config
def ensemble_many():
'use ensemble of PaSST models pretrained on Audioset with different strides mAP=.4956'
# test with: python ex_audioset.py evaluate_only with trainer.precision=16 ensemble_many
models = {
"net": DynamicIngredient("models.passt.model_ing", arch="ensemble_many", fstride=None,
tstride=None, instance_cmd="get_ensemble_model",
# don't call get_model but rather get_ensemble_model
arch_list=[
("passt_s_swa_p16_128_ap476", 10, 10),
("passt_s_swa_p16_128_ap4761", 10, 10),
("passt_s_p16_128_ap472", 10, 10),
("passt_s_p16_s12_128_ap470", 12, 12),
("passt_s_swa_p16_s12_128_ap473", 12, 12),
("passt_s_p16_s14_128_ap469", 14, 14),
("passt_s_swa_p16_s14_128_ap471", 14, 14),
("passt_s_swa_p16_s16_128_ap473", 16, 16),
("passt_s_p16_s16_128_ap468", 16, 16),
]
)
}
@ex.named_config
def ensemble_4():
'use ensemble of PaSST models pretrained on Audioset with different strides mAP=.4926'
# test with: python ex_audioset.py evaluate_only with trainer.precision=16 ensemble_many
models = {
"net": DynamicIngredient("models.passt.model_ing", arch="ensemble_many", fstride=None,
tstride=None, instance_cmd="get_ensemble_model",
# don't call get_model but rather get_ensemble_model
arch_list=[
("passt_s_swa_p16_128_ap476", 10, 10),
("passt_s_swa_p16_s12_128_ap473", 12, 12),
("passt_s_swa_p16_s14_128_ap471", 14, 14),
("passt_s_swa_p16_s16_128_ap473", 16, 16),
]
)
}
@ex.named_config
def ensemble_5():
'use ensemble of PaSST models pretrained on Audioset with different strides mAP=.49459'
# test with: python ex_audioset.py evaluate_only with trainer.precision=16 ensemble_many
models = {
"net": DynamicIngredient("models.passt.model_ing", arch="ensemble_many", fstride=None,
tstride=None, instance_cmd="get_ensemble_model",
# don't call get_model but rather get_ensemble_model
arch_list=[
("passt_s_swa_p16_128_ap476", 10, 10),
("passt_s_swa_p16_128_ap4761", 10, 10),
("passt_s_swa_p16_s12_128_ap473", 12, 12),
("passt_s_swa_p16_s14_128_ap471", 14, 14),
("passt_s_swa_p16_s16_128_ap473", 16, 16),
]
)
}
@ex.named_config
def ensemble_s16_14():
'use ensemble of two PaSST models pretrained on Audioset with stride 16 and 14 mAP=.48579'
# test with: python ex_audioset.py evaluate_only with trainer.precision=16 ensemble_s16_14
models = {
"net": DynamicIngredient("models.passt.model_ing", arch="ensemble_s16", fstride=None,
tstride=None, instance_cmd="get_ensemble_model",
# don't call get_model but rather get_ensemble_model
arch_list=[
("passt_s_swa_p16_s14_128_ap471", 14, 14),
("passt_s_swa_p16_s16_128_ap473", 16, 16),
]
)
}
@ex.named_config
def dynamic_roll():
# dynamically roll the spectrograms/waveforms
# updates the dataset config
basedataset = dict(roll=True, roll_conf=dict(axis=1, shift_range=10000)
)
# extra commands
@ex.command
def test_loaders_train_speed():
# test how fast data is being loaded from the data loaders.
itr = ex.datasets.training.get_iter()
import time
start = time.time()
print("hello")
for i, b in enumerate(itr):
if i % 20 == 0:
print(f"{i}/{len(itr)}", end="\r")
end = time.time()
print("totoal time:", end - start)
start = time.time()
print("retry:")
for i, b in enumerate(itr):
if i % 20 == 0:
print(f"{i}/{len(itr)}", end="\r")
end = time.time()
print("totoal time:", end - start)