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from typing import Dict, Type, Callable, Optional, Union
from torch.utils.data import Dataset
from .models.modeling_utils import ProteinModel
from pathlib import Path
PathType = Union[str, Path]
def convert_model_args(model_args):
d = {}
for e in model_args:
k, v = e.split("=")
try:
v = int(v)
except:
try:
v = float(v)
except:
v = str(v)
d[k] = v
return d
class TAPETaskSpec:
"""
Attributes
----------
name (str):
The name of the TAPE task
dataset (Type[Dataset]):
The dataset used in the TAPE task
num_labels (int):
number of labels used if this is a classification task
models (Dict[str, ProteinModel]):
The set of models that can be used for this task. Default: {}.
"""
def __init__(self,
name: str,
dataset: Type[Dataset],
num_labels: int = -1,
models: Optional[Dict[str, Type[ProteinModel]]] = None):
self.name = name
self.dataset = dataset
self.num_labels = num_labels
self.models = models if models is not None else {}
def register_model(self, model_name: str, model_cls: Optional[Type[ProteinModel]] = None):
if model_cls is not None:
if model_name in self.models:
raise KeyError(
f"A model with name '{model_name}' is already registered for this task")
self.models[model_name] = model_cls
return model_cls
else:
return lambda model_cls: self.register_model(model_name, model_cls)
def get_model(self, model_name: str) -> Type[ProteinModel]:
return self.models[model_name]
class Registry:
r"""Class for registry object which acts as the
central repository for TAPE."""
task_name_mapping: Dict[str, TAPETaskSpec] = {}
metric_name_mapping: Dict[str, Callable] = {}
@classmethod
def register_task(cls,
task_name: str,
num_labels: int = -1,
dataset: Optional[Type[Dataset]] = None,
models: Optional[Dict[str, Type[ProteinModel]]] = None):
""" Register a a new TAPE task. This creates a new TAPETaskSpec.
Args:
task_name (str): The name of the TAPE task.
num_labels (int): Number of labels used if this is a classification task. If this
is not a classification task, simply leave the default as -1.
dataset (Type[Dataset]): The dataset used in the TAPE task.
models (Optional[Dict[str, ProteinModel]]): The set of models that can be used for
this task. If you do not pass this argument, you can register models to the task
later by using `registry.register_task_model`. Default: {}.
Examples:
There are two ways of registering a new task. First, one can define the task by simply
declaring all the components, and then calling the register method, like so:
class SecondaryStructureDataset(Dataset):
...
class ProteinBertForSequenceToSequenceClassification():
...
registry.register_task(
'secondary_structure', 3, SecondaryStructureDataset,
{'transformer': ProteinBertForSequenceToSequenceClassification})
This will register a new task, 'secondary_structure', with a single model. More models
can be added with `registry.register_task_model`. Alternatively, this can be used as a
decorator:
@registry.regsiter_task('secondary_structure', 3)
class SecondaryStructureDataset(Dataset):
...
@registry.register_task_model('secondary_structure', 'transformer')
class ProteinBertForSequenceToSequenceClassification():
...
These two pieces of code are exactly equivalent, in terms of the resulting registry
state.
"""
if dataset is not None:
if models is None:
models = {}
task_spec = TAPETaskSpec(task_name, dataset, num_labels, models)
return cls.register_task_spec(task_name, task_spec).dataset
else:
return lambda dataset: cls.register_task(task_name, num_labels, dataset, models)
@classmethod
def register_task_spec(cls, task_name: str, task_spec: Optional[TAPETaskSpec] = None):
""" Registers a task_spec directly. If you find it easier to actually create a
TAPETaskSpec manually, and then register it, feel free to use this method,
but otherwise it is likely easier to use `registry.register_task`.
"""
if task_spec is not None:
if task_name in cls.task_name_mapping:
raise KeyError(f"A task with name '{task_name}' is already registered")
cls.task_name_mapping[task_name] = task_spec
return task_spec
else:
return lambda task_spec: cls.register_task_spec(task_name, task_spec)
@classmethod
def register_task_model(cls,
task_name: str,
model_name: str,
model_cls: Optional[Type[ProteinModel]] = None):
r"""Register a specific model to a task with the provided model name.
The task must already be in the registry - you cannot register a
model to an unregistered task.
Args:
task_name (str): Name of task to which to register the model.
model_name (str): Name of model to use when registering task, this
is the name that you will use to refer to the model on the
command line.
model_cls (Type[ProteinModel]): The model to register.
Examples:
As with `registry.register_task`, this can both be used as a regular
python function, and as a decorator. For example this:
class ProteinBertForSequenceToSequenceClassification():
...
registry.register_task_model(
'secondary_structure', 'transformer',
ProteinBertForSequenceToSequenceClassification)
and as a decorator:
@registry.register_task_model('secondary_structure', 'transformer')
class ProteinBertForSequenceToSequenceClassification():
...
are both equivalent.
"""
if task_name not in cls.task_name_mapping:
raise KeyError(
f"Tried to register a task model for an unregistered task: {task_name}. "
f"Make sure to register the task {task_name} first.")
return cls.task_name_mapping[task_name].register_model(model_name, model_cls)
@classmethod
def register_metric(cls, name: str) -> Callable[[Callable], Callable]:
r"""Register a metric to registry with key 'name'
Args:
name: Key with which the metric will be registered.
Usage::
from tape.registry import registry
@registry.register_metric('mse')
def mean_squred_error(inputs, outputs):
...
"""
def wrap(fn: Callable) -> Callable:
assert callable(fn), "All metrics must be callable"
cls.metric_name_mapping[name] = fn
return fn
return wrap
@classmethod
def get_task_spec(cls, name: str) -> TAPETaskSpec:
return cls.task_name_mapping[name]
@classmethod
def get_metric(cls, name: str) -> Callable:
return cls.metric_name_mapping[name]
@classmethod
def get_task_model(cls,
model_name: str,
task_name: str,
config_file: Optional[PathType] = None,
load_dir: Optional[PathType] = None,
model_args = None) -> ProteinModel:
""" Create a TAPE task model, either from scratch or from a pretrained model.
This is mostly a helper function that evaluates the if statements in a
sensible order if you pass all three of the arguments.
Args:
model_name (str): Which type of model to create (e.g. transformer, unirep, ...)
task_name (str): The TAPE task for which to create a model
config_file (str, optional): A json config file that specifies hyperparameters
load_dir (str, optional): A save directory for a pretrained model
Returns:
model (ProteinModel): A TAPE task model
"""
task_spec = registry.get_task_spec(task_name)
model_cls = task_spec.get_model(model_name)
if load_dir is not None:
model = model_cls.from_pretrained(load_dir, num_labels=task_spec.num_labels)
else:
config_class = model_cls.config_class
if config_file is not None:
config = config_class.from_json_file(config_file)
else:
config = config_class()
if model_args:
model_args = convert_model_args(model_args)
for k,v in model_args.items():
if k in config.__dict__ and type(config.__dict__[k])==type(v):
setattr(config, k, v)
else:
raise ValueError(f"model arg {k} not in config or of the same type as default")
config.num_labels = task_spec.num_labels
model = model_cls(config)
return model
registry = Registry()
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