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[dev] Config Registration Functions in __init__.py #72

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[dev] Config Registration Functions in __init__.py #72

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e6836be
optim config registration function
romesco Apr 22, 2021
f5dad49
reformat example
romesco Apr 22, 2021
b02b193
add test, remove example
romesco Apr 22, 2021
c95ca8d
use ConfigStoreWithProvider
romesco Apr 23, 2021
004011e
add losses to registration
romesco Apr 23, 2021
ceada17
add utils/data to registration
romesco Apr 23, 2021
fe41144
add hierarchical registration call
romesco Apr 23, 2021
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85 changes: 85 additions & 0 deletions hydra-configs-torch/hydra_configs/torch/nn/modules/__init__.py
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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# flake8: noqa
from .loss import BCELossConf
from .loss import BCEWithLogitsLossConf
from .loss import CosineEmbeddingLossConf
from .loss import CTCLossConf
from .loss import L1LossConf
from .loss import HingeEmbeddingLossConf
from .loss import KLDivLossConf
from .loss import MarginRankingLossConf
from .loss import MSELossConf
from .loss import MultiLabelMarginLossConf
from .loss import MultiLabelSoftMarginLossConf
from .loss import MultiMarginLossConf
from .loss import NLLLossConf
from .loss import NLLLoss2dConf
from .loss import PoissonNLLLossConf
from .loss import SmoothL1LossConf
from .loss import SoftMarginLossConf
from .loss import TripletMarginLossConf
Comment on lines +3 to +20
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feels like this would better be inside loss.py and __init__.py would just call it from its register function.

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It makes sense there, but loss.py is a configen output. This is manual code.

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maybe we should create a convention:

loss.py # generated
loss_handcoded.py # manual additions.

At this point, the __init__.py file can import them both (and possibly also export from both, making them appear like one file).

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not a bad idea. I think we need a convention for handling manual configs anyways.

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here is an even better idea.
We can add support to custom import to configen at the top and the bottom of the generated configs.
Allowing automatic import of user provided files if they exist.

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@romesco romesco Apr 26, 2021 •
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Could you elaborate on this?

Do you mean chain the 'manual' file to the 'generated' one through importing that is automatically added by configen if a field is passed in the configen conf yaml?

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@omry omry Apr 27, 2021 •
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not exactly.
I mean importing if a file with a specific name exists.

generated/foo.py

try:
 from . import foo_header
except ImportError:
  pass

# generated code here

try:
 from . import foo_footer
except ImportError:
  pass

This way, you can add manual foo_footer.py and foo_header.py files that will get imported automatically when the user imports foo.py.

from hydra.core.config_store import ConfigStoreWithProvider


def register():
with ConfigStoreWithProvider("torch") as cs:
cs.store(group="torch/nn/modules/loss", name="bceloss", node=BCELossConf)
cs.store(
group="torch/nn/modules/loss",
name="bcewithlogitsloss",
node=BCEWithLogitsLossConf,
)
cs.store(
group="torch/nn/modules/loss",
name="cosineembeddingloss",
node=CosineEmbeddingLossConf,
)
cs.store(group="torch/nn/modules/loss", name="ctcloss", node=CTCLossConf)
cs.store(group="torch/nn/modules/loss", name="l1loss", node=L1LossConf)
cs.store(
group="torch/nn/modules/loss",
name="hingeembeddingloss",
node=HingeEmbeddingLossConf,
)
cs.store(group="torch/nn/modules/loss", name="kldivloss", node=KLDivLossConf)
cs.store(
group="torch/nn/modules/loss",
name="marginrankingloss",
node=MarginRankingLossConf,
)
cs.store(group="torch/nn/modules/loss", name="mseloss", node=MSELossConf)
cs.store(
group="torch/nn/modules/loss",
name="multilabelmarginloss",
node=MultiLabelMarginLossConf,
)
cs.store(
group="torch/nn/modules/loss",
name="multilabelsoftmarginloss",
node=MultiLabelSoftMarginLossConf,
)
cs.store(
group="torch/nn/modules/loss",
name="multimarginloss",
node=MultiMarginLossConf,
)
cs.store(group="torch/nn/modules/loss", name="nllloss", node=NLLLossConf)
cs.store(group="torch/nn/modules/loss", name="nllloss2d", node=NLLLoss2dConf)
cs.store(
group="torch/nn/modules/loss",
name="poissonnllloss",
node=PoissonNLLLossConf,
)
cs.store(
group="torch/nn/modules/loss", name="smoothl1loss", node=SmoothL1LossConf
)
cs.store(
group="torch/nn/modules/loss",
name="softmarginloss",
node=SoftMarginLossConf,
)
cs.store(
group="torch/nn/modules/loss",
name="tripletmarginloss",
node=TripletMarginLossConf,
)
18 changes: 18 additions & 0 deletions hydra-configs-torch/hydra_configs/torch/optim/__init__.py
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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# flake8: noqa
# Mirrors torch/optim __init__ to allow for symmetric import structure
from .adadelta import AdadeltaConf
Expand All @@ -14,6 +15,23 @@
from .lbfgs import LBFGSConf
from . import lr_scheduler

from hydra.core.config_store import ConfigStoreWithProvider


def register():
with ConfigStoreWithProvider("torch") as cs:
cs.store(group="torch/optim", name="adadelta", node=AdadeltaConf)
cs.store(group="torch/optim", name="adagrad", node=AdagradConf)
cs.store(group="torch/optim", name="adam", node=AdamConf)
cs.store(group="torch/optim", name="adamw", node=AdamWConf)
cs.store(group="torch/optim", name="sparseadam", node=SparseAdamConf)
cs.store(group="torch/optim", name="adamax", node=AdamaxConf)
cs.store(group="torch/optim", name="asgd", node=ASGDConf)
cs.store(group="torch/optim", name="sgd", node=SGDConf)
cs.store(group="torch/optim", name="lbfgs", node=LBFGSConf)
cs.store(group="torch/optim", name="rprop", node=RpropConf)
cs.store(group="torch/optim", name="rmsprop", node=RMSpropConf)


del adadelta
del adagrad
Expand Down
37 changes: 37 additions & 0 deletions hydra-configs-torch/tests/test_register_losses.py
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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
from hydra.core.config_store import ConfigStore
import hydra_configs.torch.nn.modules

cs = ConfigStore()


# registers all 'base' optimizer configs with configstore instance
hydra_configs.torch.nn.modules.register()

expected = set(
[
"bceloss",
"bcewithlogitsloss",
"cosineembeddingloss",
"ctcloss",
"l1loss",
"hingeembeddingloss",
"kldivloss",
"marginrankingloss",
"mseloss",
"multilabelmarginloss",
"multilabelsoftmarginloss",
"multimarginloss",
"nllloss",
"nllloss2d",
"poissonnllloss",
"smoothl1loss",
"softmarginloss",
"tripletmarginloss",
]
)


def test_instantiate_classes() -> None:
actual = set([conf.split(".yaml")[0] for conf in cs.list("torch/nn/modules/loss")])
assert not actual ^ expected
29 changes: 29 additions & 0 deletions hydra-configs-torch/tests/test_register_optimizers.py
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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
from hydra.core.config_store import ConfigStore
import hydra_configs.torch.optim

cs = ConfigStore()

# registers all 'base' optimizer configs with configstore instance
hydra_configs.torch.optim.register()

expected = set(
[
"adadelta",
"adagrad",
"adam",
"adamw",
"sparseadam",
"adamax",
"asgd",
"sgd",
"lbfgs",
"rprop",
"rmsprop",
]
)


def test_instantiate_classes() -> None:
actual = set([conf.split(".yaml")[0] for conf in cs.list("torch/optim")])
assert not actual ^ expected