# Copyright (c) 2021-2022, InterDigital Communications, Inc
# All rights reserved.
# Redistribution and use in source and binary forms, with or without
# modification, are permitted (subject to the limitations in the disclaimer
# below) provided that the following conditions are met:
# * Redistributions of source code must retain the above copyright notice,
# this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
# * Neither the name of InterDigital Communications, Inc nor the names of its
# contributors may be used to endorse or promote products derived from this
# software without specific prior written permission.
# NO EXPRESS OR IMPLIED LICENSES TO ANY PARTY'S PATENT RIGHTS ARE GRANTED BY
# THIS LICENSE. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND
# CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT
# NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
# PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR
# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
# OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR
# OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
# ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
from typing import Callable, Dict, Type, TypeVar
from torch import optim
from torch.optim import lr_scheduler
from compressai.typing import (
TCriterion,
TDataset,
TModel,
TModule,
TOptimizer,
TScheduler,
)
CRITERIONS: Dict[str, Callable[..., TCriterion]] = {}
DATASETS: Dict[str, Callable[..., TDataset]] = {}
MODELS: Dict[str, Callable[..., TModel]] = {}
MODULES: Dict[str, Callable[..., TModule]] = {}
OPTIMIZERS: Dict[str, Callable[..., TOptimizer]] = {
k: v for k, v in optim.__dict__.items() if k[0].isupper()
}
SCHEDULERS: Dict[str, Callable[..., TScheduler]] = {
k: v for k, v in lr_scheduler.__dict__.items() if k[0].isupper()
}
TCriterion_b = TypeVar("TCriterion_b", bound=TCriterion)
TDataset_b = TypeVar("TDataset_b", bound=TDataset)
TModel_b = TypeVar("TModel_b", bound=TModel)
TModule_b = TypeVar("TModule_b", bound=TModule)
TOptimizer_b = TypeVar("TOptimizer_b", bound=TOptimizer)
TScheduler_b = TypeVar("TScheduler_b", bound=TScheduler)
[docs]def register_criterion(name: str):
"""Decorator for registering a criterion."""
def decorator(cls: Type[TCriterion_b]) -> Type[TCriterion_b]:
CRITERIONS[name] = cls
return cls
return decorator
[docs]def register_dataset(name: str):
"""Decorator for registering a dataset."""
def decorator(cls: Type[TDataset_b]) -> Type[TDataset_b]:
DATASETS[name] = cls
return cls
return decorator
[docs]def register_model(name: str):
"""Decorator for registering a model."""
def decorator(cls: Type[TModel_b]) -> Type[TModel_b]:
MODELS[name] = cls
return cls
return decorator
[docs]def register_module(name: str):
"""Decorator for registering a module."""
def decorator(cls: Type[TModule_b]) -> Type[TModule_b]:
MODULES[name] = cls
return cls
return decorator
[docs]def register_optimizer(name: str):
"""Decorator for registering a optimizer."""
def decorator(cls: Callable[..., TOptimizer_b]) -> Callable[..., TOptimizer_b]:
OPTIMIZERS[name] = cls
return cls
return decorator
[docs]def register_scheduler(name: str):
"""Decorator for registering a scheduler."""
def decorator(cls: Type[TScheduler_b]) -> Type[TScheduler_b]:
SCHEDULERS[name] = cls
return cls
return decorator
