def __init__(
self,
*args,
**kwargs,
):
self._threshold = kwargs.get("threshold", None)
self.__instantiate_transform(kwargs)
BaseDatasetSamplerMixin.__init__(self, *args, **kwargs)
BaseTasksMixin.__init__(self, *args, **kwargs)
self.clean_kwargs(kwargs)
LightningDataModule.__init__(self, *args, **kwargs)
self.dataset_train = None
self.dataset_val = None
self.dataset_test = None
self._seed = 42
self._num_workers = 2
self._shuffle = True
self._drop_last = False
self._pin_memory = True
self._follow_batch = []
self._hyper_parameters = {}
def __init__(
self,
train_sequences: List[str],
validation_sequences: List[str],
alphabet: AlphabetDataLoader,
masking_ratio: float,
masking_prob: float,
random_token_prob: float,
num_workers: int,
toks_per_batch: int,
crop_sizes: Tuple[int, int] = (512, 1024),
):
LightningDataModule.__init__(self)
self._train_sequences = train_sequences
self._validation_sequences = validation_sequences
self._alphabet = alphabet
self._masking_ratio = masking_ratio
self._masking_prob = masking_prob
self._random_token_prob = random_token_prob
self._num_workers = num_workers
self._toks_per_batch = toks_per_batch
self._crop_sizes = crop_sizes
def __post_init__(self,
observation_space: gym.Space = None,
action_space: gym.Space = None,
reward_space: gym.Space = None):
""" Initializes the fields of the setting that weren't set from the
command-line.
"""
logger.debug(f"__post_init__ of Setting")
if len(self.train_transforms) == 1 and isinstance(self.train_transforms[0], list):
self.train_transforms = self.train_transforms[0]
if len(self.val_transforms) == 1 and isinstance(self.val_transforms[0], list):
self.val_transforms = self.val_transforms[0]
if len(self.test_transforms) == 1 and isinstance(self.test_transforms[0], list):
self.test_transforms = self.test_transforms[0]
# Actually compose the list of Transforms or callables into a single transform.
self.train_transforms: Compose = Compose(self.train_transforms)
self.val_transforms: Compose = Compose(self.val_transforms)
self.test_transforms: Compose = Compose(self.test_transforms)
LightningDataModule.__init__(self,
train_transforms=self.train_transforms,
val_transforms=self.val_transforms,
test_transforms=self.test_transforms,
)
self._observation_space = observation_space
self._action_space = action_space
self._reward_space = reward_space
# TODO: It's a bit confusing to also have a `config` attribute on the
# Setting. Might want to change this a bit.
self.config: Config = None
self.train_env: Environment = None # type: ignore
self.val_env: Environment = None # type: ignore
self.test_env: Environment = None # type: ignore
def __post_init__(
self,
observation_space: gym.Space = None,
action_space: gym.Space = None,
reward_space: gym.Space = None,
):
""" Initializes the fields of the setting that weren't set from the
command-line.
"""
logger.debug("__post_init__ of Setting")
# BUG: simple-parsing sometimes parses a list with a single item, itself the
# list of transforms. Not sure if this still happens.
def is_list_of_list(v: Any) -> bool:
return isinstance(v, list) and len(v) == 1 and isinstance(v[0], list)
if is_list_of_list(self.train_transforms):
self.train_transforms = self.train_transforms[0]
if is_list_of_list(self.val_transforms):
self.val_transforms = self.val_transforms[0]
if is_list_of_list(self.test_transforms):
self.test_transforms = self.test_transforms[0]
if all(
t is None
for t in [
self.transforms,
self.train_transforms,
self.val_transforms,
self.test_transforms,
]
):
# Use these two transforms by default if no transforms are passed at all.
# TODO: Remove this after the competition perhaps.
self.transforms = Compose([Transforms.to_tensor, Transforms.three_channels])
# If the constructor is called with just the `transforms` argument, like this:
# <SomeSetting>(dataset="bob", transforms=foo_transform)
# Then we use this value as the default for the train, val and test transforms.
if self.transforms and not any(
[self.train_transforms, self.val_transforms, self.test_transforms]
):
if not isinstance(self.transforms, list):
self.transforms = Compose([self.transforms])
self.train_transforms = self.transforms.copy()
self.val_transforms = self.transforms.copy()
self.test_transforms = self.transforms.copy()
if self.train_transforms is not None and not isinstance(
self.train_transforms, list
):
self.train_transforms = [self.train_transforms]
if self.val_transforms is not None and not isinstance(
self.val_transforms, list
):
self.val_transforms = [self.val_transforms]
if self.test_transforms is not None and not isinstance(
self.test_transforms, list
):
self.test_transforms = [self.test_transforms]
# Actually compose the list of Transforms or callables into a single transform.
self.train_transforms: Compose = Compose(self.train_transforms or [])
self.val_transforms: Compose = Compose(self.val_transforms or [])
self.test_transforms: Compose = Compose(self.test_transforms or [])
LightningDataModule.__init__(
self,
train_transforms=self.train_transforms,
val_transforms=self.val_transforms,
test_transforms=self.test_transforms,
)
self._observation_space = observation_space
self._action_space = action_space
self._reward_space = reward_space
# TODO: It's a bit confusing to also have a `config` attribute on the
# Setting. Might want to change this a bit.
self.config: Config = None
self.train_env: Environment = None # type: ignore
self.val_env: Environment = None # type: ignore
self.test_env: Environment = None # type: ignore