[Datumaro] Label remapping transform (#1233)

* Add label remapping transform

* Apply transforms before project saving

* Refactor voc converter

datumaro/datumaro/components/project.py

@@ -634,6 +634,8 @@ class ProjectDataset(Dataset):
         return self._sources
 
     def _save_branch_project(self, extractor, save_dir=None):
+        extractor = Dataset.from_extractors(extractor) # apply lazy transforms
+
         # NOTE: probably this function should be in the ViewModel layer
         save_dir = osp.abspath(save_dir)
         if save_dir:

datumaro/datumaro/plugins/transforms.py

@@ -3,6 +3,7 @@
 #
 # SPDX-License-Identifier: MIT
 
+from enum import Enum
 import logging as log
 import os.path as osp
 import random
@@ -10,7 +11,9 @@ import random
 import pycocotools.mask as mask_utils
 
 from datumaro.components.extractor import (Transform, AnnotationType,
-    RleMask, Polygon, Bbox)
+    RleMask, Polygon, Bbox,
+    LabelCategories, MaskCategories, PointsCategories
+)
 from datumaro.components.cli_plugin import CliPlugin
 import datumaro.util.mask_tools as mask_tools
 from datumaro.util.annotation_tools import find_group_leader, find_instances
@@ -46,7 +49,7 @@ class CropCoveredSegments(Transform, CliPlugin):
                 segments.append(s.points)
             elif s.type == AnnotationType.mask:
                 if isinstance(s, RleMask):
-                    rle = s._rle
+                    rle = s.rle
                 else:
                     rle = mask_tools.mask_to_rle(s.image)
                 segments.append(rle)
@@ -365,3 +368,116 @@ class IdFromImageName(Transform, CliPlugin):
         if item.has_image and item.image.filename:
             name = osp.splitext(item.image.filename)[0]
         return self.wrap_item(item, id=name)
+
+class RemapLabels(Transform, CliPlugin):
+    DefaultAction = Enum('DefaultAction', ['keep', 'delete'])
+
+    @staticmethod
+    def _split_arg(s):
+        parts = s.split(':')
+        if len(parts) != 2:
+            import argparse
+            raise argparse.ArgumentTypeError()
+        return (parts[0], parts[1])
+
+    @classmethod
+    def build_cmdline_parser(cls, **kwargs):
+        parser = super().build_cmdline_parser(**kwargs)
+        parser.add_argument('-l', '--label', action='append',
+            type=cls._split_arg, dest='mapping',
+            help="Label in the form of: '<src>:<dst>' (repeatable)")
+        parser.add_argument('--default',
+            choices=[a.name for a in cls.DefaultAction],
+            default=cls.DefaultAction.keep.name,
+            help="Action for unspecified labels")
+        return parser
+
+    def __init__(self, extractor, mapping, default=None):
+        super().__init__(extractor)
+
+        assert isinstance(default, (str, self.DefaultAction))
+        if isinstance(default, str):
+            default = self.DefaultAction[default]
+
+        assert isinstance(mapping, (dict, list))
+        if isinstance(mapping, list):
+            mapping = dict(mapping)
+
+        self._categories = {}
+
+        src_label_cat = self._extractor.categories().get(AnnotationType.label)
+        if src_label_cat is not None:
+            self._make_label_id_map(src_label_cat, mapping, default)
+
+        src_mask_cat = self._extractor.categories().get(AnnotationType.mask)
+        if src_mask_cat is not None:
+            assert src_label_cat is not None
+            dst_mask_cat = MaskCategories(attributes=src_mask_cat.attributes)
+            dst_mask_cat.colormap = {
+                id: src_mask_cat.colormap[id]
+                for id, _ in enumerate(src_label_cat.items)
+                if self._map_id(id) or id == 0
+            }
+            self._categories[AnnotationType.mask] = dst_mask_cat
+
+        src_points_cat = self._extractor.categories().get(AnnotationType.points)
+        if src_points_cat is not None:
+            assert src_label_cat is not None
+            dst_points_cat = PointsCategories(attributes=src_points_cat.attributes)
+            dst_points_cat.items = {
+                id: src_points_cat.items[id]
+                for id, item in enumerate(src_label_cat.items)
+                if self._map_id(id) or id == 0
+            }
+            self._categories[AnnotationType.points] = dst_points_cat
+
+    def _make_label_id_map(self, src_label_cat, label_mapping, default_action):
+        dst_label_cat = LabelCategories(attributes=src_label_cat.attributes)
+        id_mapping = {}
+        for src_index, src_label in enumerate(src_label_cat.items):
+            dst_label = label_mapping.get(src_label.name)
+            if not dst_label and default_action == self.DefaultAction.keep:
+                dst_label = src_label.name # keep unspecified as is
+            if not dst_label:
+                continue
+
+            dst_index = dst_label_cat.find(dst_label)[0]
+            if dst_index is None:
+                dst_label_cat.add(dst_label,
+                    src_label.parent, src_label.attributes)
+                dst_index = dst_label_cat.find(dst_label)[0]
+            id_mapping[src_index] = dst_index
+
+        if log.getLogger().isEnabledFor(log.DEBUG):
+            log.debug("Label mapping:")
+            for src_id, src_label in enumerate(src_label_cat.items):
+                if id_mapping.get(src_id):
+                    log.debug("#%s '%s' -> #%s '%s'",
+                        src_id, src_label.name, id_mapping[src_id],
+                        dst_label_cat.items[id_mapping[src_id]].name
+                    )
+                else:
+                    log.debug("#%s '%s' -> <deleted>", src_id, src_label.name)
+
+        self._map_id = lambda src_id: id_mapping.get(src_id, None)
+        self._categories[AnnotationType.label] = dst_label_cat
+
+    def categories(self):
+        return self._categories
+
+    def transform_item(self, item):
+        # TODO: provide non-inplace version
+        annotations = []
+        for ann in item.annotations:
+            if ann.type in { AnnotationType.label, AnnotationType.mask,
+                AnnotationType.points, AnnotationType.polygon,
+                AnnotationType.polyline, AnnotationType.bbox
+            } and ann.label is not None:
+                conv_label = self._map_id(ann.label)
+                if conv_label is not None:
+                    ann._label = conv_label
+                    annotations.append(ann)
+            else:
+                annotations.append(ann)
+        item._annotations = annotations
+        return item

datumaro/datumaro/plugins/voc_format/converter.py

@@ -53,14 +53,13 @@ LabelmapType = Enum('LabelmapType', ['voc', 'source', 'guess'])
 class _Converter:
     def __init__(self, extractor, save_dir,
             tasks=None, apply_colormap=True, save_images=False, label_map=None):
-        assert tasks is None or isinstance(tasks, (VocTask, list))
+        assert tasks is None or isinstance(tasks, (VocTask, list, set))
         if tasks is None:
-            tasks = list(VocTask)
+            tasks = set(VocTask)
         elif isinstance(tasks, VocTask):
-            tasks = [tasks]
+            tasks = {tasks}
         else:
-            tasks = [t if t in VocTask else VocTask[t] for t in tasks]
-
+            tasks = set(t if t in VocTask else VocTask[t] for t in tasks)
         self._tasks = tasks
 
         self._extractor = extractor
@@ -259,10 +258,10 @@ class _Converter:
                         if len(actions_elem) != 0:
                             obj_elem.append(actions_elem)
 
-                    if set(self._tasks) & set([None,
+                    if self._tasks & {None,
                             VocTask.detection,
                             VocTask.person_layout,
-                            VocTask.action_classification]):
+                            VocTask.action_classification}:
                         with open(osp.join(self._ann_dir, item.id + '.xml'), 'w') as f:
                             f.write(ET.tostring(root_elem,
                                 encoding='unicode', pretty_print=True))
@@ -302,19 +301,19 @@ class _Converter:
                     action_list[item.id] = None
                     segm_list[item.id] = None
 
-                if set(self._tasks) & set([None,
+                if self._tasks & {None,
                         VocTask.classification,
                         VocTask.detection,
                         VocTask.action_classification,
-                        VocTask.person_layout]):
+                        VocTask.person_layout}:
                     self.save_clsdet_lists(subset_name, clsdet_list)
-                    if set(self._tasks) & set([None, VocTask.classification]):
+                    if self._tasks & {None, VocTask.classification}:
                         self.save_class_lists(subset_name, class_lists)
-                if set(self._tasks) & set([None, VocTask.action_classification]):
+                if self._tasks & {None, VocTask.action_classification}:
                     self.save_action_lists(subset_name, action_list)
-                if set(self._tasks) & set([None, VocTask.person_layout]):
+                if self._tasks & {None, VocTask.person_layout}:
                     self.save_layout_lists(subset_name, layout_list)
-                if set(self._tasks) & set([None, VocTask.segmentation]):
+                if self._tasks & {None, VocTask.segmentation}:
                     self.save_segm_lists(subset_name, segm_list)
 
     def save_action_lists(self, subset_name, action_list):

datumaro/tests/test_transforms.py

@@ -3,10 +3,12 @@ import numpy as np
 from unittest import TestCase
 
 from datumaro.components.extractor import (Extractor, DatasetItem,
-    Mask, Polygon, PolyLine, Points, Bbox
+    Mask, Polygon, PolyLine, Points, Bbox, Label,
+    LabelCategories, MaskCategories, AnnotationType
 )
-from datumaro.util.test_utils import compare_datasets
+import datumaro.util.mask_tools as mask_tools
 import datumaro.plugins.transforms as transforms
+from datumaro.util.test_utils import compare_datasets
 
 
 class TransformsTest(TestCase):
@@ -361,3 +363,95 @@ class TransformsTest(TestCase):
                 ('train', -0.5),
                 ('test', 1.5),
             ])
+
+    def test_remap_labels(self):
+        class SrcExtractor(Extractor):
+            def __iter__(self):
+                return iter([
+                    DatasetItem(id=1, annotations=[
+                        # Should be remapped
+                        Label(1),
+                        Bbox(1, 2, 3, 4, label=2),
+                        Mask(image=np.array([1]), label=3),
+
+                        # Should be kept
+                        Polygon([1, 1, 2, 2, 3, 4], label=4),
+                        PolyLine([1, 3, 4, 2, 5, 6], label=None)
+                    ]),
+                ])
+
+            def categories(self):
+                label_cat = LabelCategories()
+                label_cat.add('label0')
+                label_cat.add('label1')
+                label_cat.add('label2')
+                label_cat.add('label3')
+                label_cat.add('label4')
+
+                mask_cat = MaskCategories(
+                    colormap=mask_tools.generate_colormap(5))
+
+                return {
+                    AnnotationType.label: label_cat,
+                    AnnotationType.mask: mask_cat,
+                }
+
+        class DstExtractor(Extractor):
+            def __iter__(self):
+                return iter([
+                    DatasetItem(id=1, annotations=[
+                        Label(1),
+                        Bbox(1, 2, 3, 4, label=0),
+                        Mask(image=np.array([1]), label=1),
+
+                        Polygon([1, 1, 2, 2, 3, 4], label=2),
+                        PolyLine([1, 3, 4, 2, 5, 6], label=None)
+                    ]),
+                ])
+
+            def categories(self):
+                label_cat = LabelCategories()
+                label_cat.add('label0')
+                label_cat.add('label9')
+                label_cat.add('label4')
+
+                mask_cat = MaskCategories(colormap={
+                    k: v for k, v in mask_tools.generate_colormap(5).items()
+                    if k in { 0, 1, 3, 4 }
+                })
+
+                return {
+                    AnnotationType.label: label_cat,
+                    AnnotationType.mask: mask_cat,
+                }
+
+        actual = transforms.RemapLabels(SrcExtractor(), mapping={
+            'label1': 'label9',
+            'label2': 'label0',
+            'label3': 'label9',
+        }, default='keep')
+
+        compare_datasets(self, DstExtractor(), actual)
+
+    def test_remap_labels_delete_unspecified(self):
+        class SrcExtractor(Extractor):
+            def __iter__(self):
+                return iter([ DatasetItem(id=1, annotations=[ Label(0) ]) ])
+
+            def categories(self):
+                label_cat = LabelCategories()
+                label_cat.add('label0')
+
+                return { AnnotationType.label: label_cat }
+
+        class DstExtractor(Extractor):
+            def __iter__(self):
+                return iter([ DatasetItem(id=1, annotations=[]) ])
+
+            def categories(self):
+                return { AnnotationType.label: LabelCategories() }
+
+        actual = transforms.RemapLabels(SrcExtractor(),
+            mapping={}, default='delete')
+
+        compare_datasets(self, DstExtractor(), actual)