# Copyright (c) MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
from abc import ABCMeta, abstractmethod
from enum import Enum
from typing import Any, Dict, Sequence, Tuple, Union
logger = logging.getLogger(__name__)
[docs]class InferType(str, Enum):
"""
Type of Inference Model
Attributes:
SEGMENTATION - Segmentation Model
ANNOTATION - Annotation Model
CLASSIFICATION - Classification Model
DEEPGROW - Deepgrow Interactive Model
DEEPEDIT - DeepEdit Interactive Model
SCRIBBLES - Scribbles Model
DETECTION - Detection Model
OTHERS - Other Model Type
"""
SEGMENTATION: str = "segmentation"
ANNOTATION: str = "annotation"
CLASSIFICATION: str = "classification"
DEEPGROW: str = "deepgrow"
DEEPEDIT: str = "deepedit"
SCRIBBLES: str = "scribbles"
DETECTION: str = "detection"
OTHERS: str = "others"
[docs]class InferTask(metaclass=ABCMeta):
"""
Inference Task
"""
[docs] def __init__(
self,
type: Union[str, InferType],
labels: Union[str, None, Sequence[str], Dict[Any, Any]],
dimension: int,
description: str,
config: Union[None, Dict[str, Any]] = None,
):
"""
:param type: Type of Infer (segmentation, deepgrow etc..)
:param labels: Labels associated to this Infer
:param dimension: Input dimension
:param description: Description
:param config: K,V pairs to be part of user config
"""
self.type = type
self.labels = [] if labels is None else [labels] if isinstance(labels, str) else labels
self.dimension = dimension
self.description = description
self._config: Dict[str, Any] = {}
if config:
self._config.update(config)
[docs] def info(self) -> Dict[str, Any]:
return {
"type": self.type,
"labels": self.labels,
"dimension": self.dimension,
"description": self.description,
"config": self.config(),
}
[docs] def config(self) -> Dict[str, Any]:
return self._config
[docs] def get_path(self, validate=True):
return None
[docs] @abstractmethod
def is_valid(self) -> bool:
pass
@abstractmethod
def __call__(self, request) -> Union[Dict, Tuple[str, Dict[str, Any]]]:
pass