Convert most enum definitions from the functional style to the class …

…style (cvat-ai#290)

datumaro/cli/contexts/project/diff.py

@@ -5,28 +5,27 @@
 
 from collections import Counter
 from itertools import zip_longest
-from enum import Enum
+from enum import Enum, auto
 import logging as log
 import os
 import os.path as osp
 
 import cv2
 import numpy as np
 
-_formats = ['simple']
-
 import warnings
 with warnings.catch_warnings():
     warnings.simplefilter("ignore")
     import tensorboardX as tb
-    _formats.append('tensorboard')
 
 from datumaro.components.dataset import IDataset
 from datumaro.components.extractor import AnnotationType, LabelCategories
 from datumaro.util.image import save_image
 
 
-OutputFormat = Enum('Formats', _formats)
+class OutputFormat(Enum):
+    simple = auto()
+    tensorboard = auto()
 
 class DatasetDiffVisualizer:
     OutputFormat = OutputFormat

datumaro/components/dataset.py

@@ -5,7 +5,7 @@
 #pylint: disable=redefined-builtin
 
 from contextlib import contextmanager
-from enum import Enum
+from enum import Enum, auto
 from typing import Iterable, Iterator, Optional, Tuple, Union, Dict, List
 import logging as log
 import os
@@ -144,7 +144,10 @@ def get(self, id, subset=None):
         return self._parent.get(id, subset=subset)
 
 
-ItemStatus = Enum('ItemStatus', ['added', 'modified', 'removed'])
+class ItemStatus(Enum):
+    added = auto()
+    modified = auto()
+    removed = auto()
 
 class DatasetPatch:
     def __init__(self, data: DatasetItemStorage,

datumaro/components/extractor.py

@@ -3,7 +3,7 @@
 #
 # SPDX-License-Identifier: MIT
 
-from enum import Enum
+from enum import Enum, auto
 from glob import iglob
 from typing import Callable, Iterable, List, Dict, Optional
 import numpy as np
@@ -17,16 +17,14 @@
 from datumaro.util.attrs_util import not_empty, default_if_none
 
 
-AnnotationType = Enum('AnnotationType',
-    [
-        'label',
-        'mask',
-        'points',
-        'polygon',
-        'polyline',
-        'bbox',
-        'caption',
-    ])
+class AnnotationType(Enum):
+    label = auto()
+    mask = auto()
+    points = auto()
+    polygon = auto()
+    polyline = auto()
+    bbox = auto()
+    caption = auto()
 
 _COORDINATE_ROUNDING_DIGITS = 2
 
@@ -468,11 +466,11 @@ def add(self, label_id, labels=None, joints=None):
 
 @attrs
 class Points(_Shape):
-    Visibility = Enum('Visibility', [
-        ('absent', 0),
-        ('hidden', 1),
-        ('visible', 2),
-    ])
+    class Visibility(Enum):
+        absent = 0
+        hidden = 1
+        visible = 2
+
     _type = AnnotationType.points
 
     visibility = attrib(type=list, default=None)

datumaro/components/validator.py

@@ -3,7 +3,7 @@
 # SPDX-License-Identifier: MIT
 
 from copy import deepcopy
-from enum import Enum
+from enum import Enum, auto
 from typing import Union
 
 import numpy as np
@@ -22,9 +22,14 @@
 from datumaro.util import parse_str_enum_value
 
 
-Severity = Enum('Severity', ['warning', 'error'])
+class Severity(Enum):
+    warning = auto()
+    error = auto()
 
-TaskType = Enum('TaskType', ['classification', 'detection', 'segmentation'])
+class TaskType(Enum):
+    classification = auto()
+    detection = auto()
+    segmentation = auto()
 
 
 class _Validator(CliPlugin):

datumaro/plugins/camvid_format.py

@@ -1,13 +1,13 @@
 
-# Copyright (C) 2020 Intel Corporation
+# Copyright (C) 2020-2021 Intel Corporation
 #
 # SPDX-License-Identifier: MIT
 
 import logging as log
 import os
 import os.path as osp
 from collections import OrderedDict
-from enum import Enum
+from enum import Enum, auto
 
 import numpy as np
 
@@ -213,7 +213,9 @@ def find_sources(cls, path):
             file_filter=lambda p: osp.basename(p) != CamvidPath.LABELMAP_FILE)
 
 
-LabelmapType = Enum('LabelmapType', ['camvid', 'source'])
+class LabelmapType(Enum):
+    camvid = auto()
+    source = auto()
 
 class CamvidConverter(Converter):
     DEFAULT_IMAGE_EXT = CamvidPath.IMAGE_EXT

datumaro/plugins/cityscapes_format.py

@@ -1,13 +1,13 @@
 
-# Copyright (C) 2020 Intel Corporation
+# Copyright (C) 2020-2021 Intel Corporation
 #
 # SPDX-License-Identifier: MIT
 
 import logging as log
 import os
 import os.path as osp
 from collections import OrderedDict
-from enum import Enum
+from enum import Enum, auto
 from glob import iglob
 
 import numpy as np
@@ -200,7 +200,9 @@ def find_sources(cls, path):
             max_depth=1)
 
 
-LabelmapType = Enum('LabelmapType', ['cityscapes', 'source'])
+class LabelmapType(Enum):
+    cityscapes = auto()
+    source = auto()
 
 class CityscapesConverter(Converter):
     DEFAULT_IMAGE_EXT = '.png'

datumaro/plugins/coco_format/converter.py

@@ -1,5 +1,5 @@
 
-# Copyright (C) 2020 Intel Corporation
+# Copyright (C) 2020-2021 Intel Corporation
 #
 # SPDX-License-Identifier: MIT
 
@@ -8,7 +8,7 @@
 import numpy as np
 import os
 import os.path as osp
-from enum import Enum
+from enum import Enum, auto
 from itertools import chain, groupby
 
 import pycocotools.mask as mask_utils
@@ -24,7 +24,10 @@
 
 from .format import CocoPath, CocoTask
 
-SegmentationMode = Enum('SegmentationMode', ['guess', 'polygons', 'mask'])
+class SegmentationMode(Enum):
+    guess = auto()
+    polygons = auto()
+    mask = auto()
 
 class _TaskConverter:
     def __init__(self, context):

datumaro/plugins/coco_format/format.py

@@ -1,20 +1,19 @@
 
-# Copyright (C) 2019-2020 Intel Corporation
+# Copyright (C) 2019-2021 Intel Corporation
 #
 # SPDX-License-Identifier: MIT
 
-from enum import Enum
+from enum import Enum, auto
 
 
-CocoTask = Enum('CocoTask', [
-    'instances',
-    'person_keypoints',
-    'captions',
-    'labels', # extension, does not exist in the original COCO format
-    'image_info',
-    'panoptic',
-    'stuff',
-])
+class CocoTask(Enum):
+    instances = auto()
+    person_keypoints = auto()
+    captions = auto()
+    labels = auto() # extension, does not exist in the original COCO format
+    image_info = auto()
+    panoptic = auto()
+    stuff = auto()
 
 class CocoPath:
     IMAGES_DIR = 'images'

datumaro/plugins/icdar_format/format.py

@@ -1,15 +1,14 @@
-# Copyright (C) 2020 Intel Corporation
+# Copyright (C) 2020-2021 Intel Corporation
 #
 # SPDX-License-Identifier: MIT
 
-from enum import Enum
+from enum import Enum, auto
 
 
-IcdarTask = Enum('IcdarTask', [
-    'word_recognition',
-    'text_localization',
-    'text_segmentation',
-])
+class IcdarTask(Enum):
+    word_recognition = auto()
+    text_localization = auto()
+    text_segmentation = auto()
 
 class IcdarPath:
     IMAGE_EXT = '.png'

datumaro/plugins/kitti_format/converter.py

@@ -7,7 +7,7 @@
 import os
 import os.path as osp
 from collections import OrderedDict
-from enum import Enum
+from enum import Enum, auto
 
 import numpy as np
 
@@ -24,7 +24,9 @@
     parse_label_map, write_label_map,
 )
 
-LabelmapType = Enum('LabelmapType', ['kitti', 'source'])
+class LabelmapType(Enum):
+    kitti = auto()
+    source = auto()
 
 class KittiConverter(Converter):
     DEFAULT_IMAGE_EXT = KittiPath.IMAGE_EXT

datumaro/plugins/kitti_format/format.py

@@ -4,7 +4,7 @@
 # SPDX-License-Identifier: MIT
 
 from collections import OrderedDict
-from enum import Enum
+from enum import Enum, auto
 
 from datumaro.components.extractor import (AnnotationType,
     LabelCategories, MaskCategories
@@ -13,10 +13,9 @@
 from datumaro.util.mask_tools import generate_colormap
 
 
-KittiTask = Enum('KittiTask', [
-    'segmentation',
-    'detection'
-])
+class KittiTask(Enum):
+    segmentation = auto()
+    detection = auto()
 
 KittiLabelMap = OrderedDict([
     ('unlabeled', (0, 0, 0)),

datumaro/plugins/mots_format.py

@@ -1,4 +1,4 @@
-# Copyright (C) 2020 Intel Corporation
+# Copyright (C) 2020-2021 Intel Corporation
 #
 # SPDX-License-Identifier: MIT
 
@@ -26,12 +26,11 @@ class MotsPath:
     LABELS_FILE = 'labels.txt'
     MAX_INSTANCES = 1000
 
-MotsLabels = Enum('MotsLabels', [
-    ('background', 0),
-    ('car', 1),
-    ('pedestrian', 2),
-    ('ignored', 10),
-])
+class MotsLabels(Enum):
+    background = 0
+    car = 1
+    pedestrian = 2
+    ignored = 10
 
 class MotsPngExtractor(SourceExtractor):
     @staticmethod

datumaro/plugins/ndr.py

@@ -2,7 +2,7 @@
 #
 # SPDX-License-Identifier: MIT
 
-from enum import Enum
+from enum import Enum, auto
 import logging as log
 
 import cv2
@@ -14,11 +14,17 @@
 from datumaro.util import parse_str_enum_value
 
 
-Algorithm = Enum("Algorithm", ["gradient"]) # other algorithms will be added
+class Algorithm(Enum):
+    gradient = auto()
+    # other algorithms will be added
 
-OverSamplingMethod = Enum("OverSamplingMethod", ["random", "similarity"])
+class OverSamplingMethod(Enum):
+    random = auto()
+    similarity = auto()
 
-UnderSamplingMethod = Enum("UnderSamplingMethod", ["uniform", "inverse"])
+class UnderSamplingMethod(Enum):
+    uniform = auto()
+    inverse = auto()
 
 class NDR(Transform, CliPlugin):
     """

datumaro/plugins/sampler/algorithm/algorithm.py

@@ -2,13 +2,18 @@
 #
 # SPDX-License-Identifier: MIT
 
-from enum import Enum
+from enum import Enum, auto
 
 
-SamplingMethod = Enum("SamplingMethod",
-    ["topk", "lowk", "randk", "mixk", "randtopk"])
+class SamplingMethod(Enum):
+    topk = auto()
+    lowk = auto()
+    randk = auto()
+    mixk = auto()
+    randtopk = auto()
 
-Algorithm = Enum("Algorithm", ["entropy"])
+class Algorithm(Enum):
+    entropy = auto()
 
 class InferenceResultAnalyzer:
     """