Update CVAT formats to use changes in Datumaro API (#2794)

* update cvat formats to use datumaro changes

* Update datumaro dependency

* Add comments on datumaro install in requirements

* fix linter

* Fix test

* fix linter

* Update datumaro version to 0.1.6.1

cvat/apps/dataset_manager/formats/camvid.py

@@ -4,7 +4,7 @@
 
 from tempfile import TemporaryDirectory
 
-from datumaro.components.project import Dataset
+from datumaro.components.dataset import Dataset
 from pyunpack import Archive
 
 from cvat.apps.dataset_manager.bindings import (CvatTaskDataExtractor,
@@ -17,16 +17,15 @@ from .utils import make_colormap
 
 @exporter(name='CamVid', ext='ZIP', version='1.0')
 def _export(dst_file, task_data, save_images=False):
-    extractor = CvatTaskDataExtractor(task_data, include_images=save_images)
-    envt = dm_env.transforms
-    extractor = extractor.transform(envt.get('polygons_to_masks'))
-    extractor = extractor.transform(envt.get('boxes_to_masks'))
-    extractor = extractor.transform(envt.get('merge_instance_segments'))
-    extractor = Dataset.from_extractors(extractor) # apply lazy transforms
+    dataset = Dataset.from_extractors(CvatTaskDataExtractor(
+        task_data, include_images=save_images), env=dm_env)
+    dataset.transform('polygons_to_masks')
+    dataset.transform('boxes_to_masks')
+    dataset.transform('merge_instance_segments')
     label_map = make_colormap(task_data)
     with TemporaryDirectory() as temp_dir:
-        dm_env.converters.get('camvid').convert(extractor,
-            save_dir=temp_dir, save_images=save_images, apply_colormap=True,
+        dataset.export(temp_dir, 'camvid',
+            save_images=save_images, apply_colormap=True,
             label_map={label: label_map[label][0] for label in label_map})
 
         make_zip_archive(temp_dir, dst_file)
@@ -36,7 +35,6 @@ def _import(src_file, task_data):
     with TemporaryDirectory() as tmp_dir:
         Archive(src_file.name).extractall(tmp_dir)
 
-        dataset = dm_env.make_importer('camvid')(tmp_dir).make_dataset()
-        masks_to_polygons = dm_env.transforms.get('masks_to_polygons')
-        dataset = dataset.transform(masks_to_polygons)
+        dataset = Dataset.import_from(tmp_dir, 'camvid', env=dm_env)
+        dataset.transform('masks_to_polygons')
         import_dm_annotations(dataset, task_data)

cvat/apps/dataset_manager/formats/coco.py

@@ -5,7 +5,8 @@
 import zipfile
 from tempfile import TemporaryDirectory
 
-from datumaro.components.project import Dataset
+from datumaro.components.dataset import Dataset
+
 from cvat.apps.dataset_manager.bindings import CvatTaskDataExtractor, \
     import_dm_annotations
 from cvat.apps.dataset_manager.util import make_zip_archive
@@ -15,11 +16,10 @@ from .registry import dm_env, exporter, importer
 
 @exporter(name='COCO', ext='ZIP', version='1.0')
 def _export(dst_file, task_data, save_images=False):
-    extractor = CvatTaskDataExtractor(task_data, include_images=save_images)
-    extractor = Dataset.from_extractors(extractor) # apply lazy transforms
+    dataset = Dataset.from_extractors(CvatTaskDataExtractor(
+        task_data, include_images=save_images), env=dm_env)
     with TemporaryDirectory() as temp_dir:
-        dm_env.converters.get('coco_instances').convert(extractor,
-            save_dir=temp_dir, save_images=save_images)
+        dataset.export(temp_dir, 'coco_instances', save_images=save_images)
 
         make_zip_archive(temp_dir, dst_file)
 
@@ -29,8 +29,9 @@ def _import(src_file, task_data):
         with TemporaryDirectory() as tmp_dir:
             zipfile.ZipFile(src_file).extractall(tmp_dir)
 
-            dataset = dm_env.make_importer('coco')(tmp_dir).make_dataset()
+            dataset = Dataset.import_from(tmp_dir, 'coco', env=dm_env)
             import_dm_annotations(dataset, task_data)
     else:
-        dataset = dm_env.make_extractor('coco_instances', src_file.name)
+        dataset = Dataset.import_from(src_file.name,
+            'coco_instances', env=dm_env)
         import_dm_annotations(dataset, task_data)

cvat/apps/dataset_manager/formats/cvat.py

@@ -9,10 +9,11 @@ from collections import OrderedDict
 from glob import glob
 from tempfile import TemporaryDirectory
 
+from datumaro.components.extractor import DatasetItem
+
 from cvat.apps.dataset_manager.bindings import match_dm_item
 from cvat.apps.dataset_manager.util import make_zip_archive
 from cvat.apps.engine.frame_provider import FrameProvider
-from datumaro.components.extractor import DatasetItem
 
 from .registry import exporter, importer
 

cvat/apps/dataset_manager/formats/imagenet.py

@@ -3,12 +3,12 @@
 # SPDX-License-Identifier: MIT
 
 import os.path as osp
-from glob import glob
-
 import zipfile
+from glob import glob
 from tempfile import TemporaryDirectory
 
-from datumaro.components.project import Dataset
+from datumaro.components.dataset import Dataset
+
 from cvat.apps.dataset_manager.bindings import CvatTaskDataExtractor, \
     import_dm_annotations
 from cvat.apps.dataset_manager.util import make_zip_archive
@@ -18,15 +18,13 @@ from .registry import dm_env, exporter, importer
 
 @exporter(name='ImageNet', ext='ZIP', version='1.0')
 def _export(dst_file, task_data, save_images=False):
-    extractor = CvatTaskDataExtractor(task_data, include_images=save_images)
-    extractor = Dataset.from_extractors(extractor) # apply lazy transform
+    dataset = Dataset.from_extractors(CvatTaskDataExtractor(
+        task_data, include_images=save_images), env=dm_env)
     with TemporaryDirectory() as temp_dir:
         if save_images:
-            dm_env.converters.get('imagenet').convert(extractor,
-                save_dir=temp_dir, save_images=save_images)
+            dataset.export(temp_dir, 'imagenet', save_images=save_images)
         else:
-            dm_env.converters.get('imagenet_txt').convert(extractor,
-                save_dir=temp_dir, save_images=save_images)
+            dataset.export(temp_dir, 'imagenet_txt', save_images=save_images)
 
         make_zip_archive(temp_dir, dst_file)
 
@@ -35,7 +33,7 @@ def _import(src_file, task_data):
     with TemporaryDirectory() as tmp_dir:
         zipfile.ZipFile(src_file).extractall(tmp_dir)
         if glob(osp.join(tmp_dir, '*.txt')):
-            dataset = dm_env.make_importer('imagenet_txt')(tmp_dir).make_dataset()
+            dataset = Dataset.import_from(tmp_dir, 'imagenet_txt', env=dm_env)
         else:
-            dataset = dm_env.make_importer('imagenet')(tmp_dir).make_dataset()
+            dataset = Dataset.import_from(tmp_dir, 'imagenet', env=dm_env)
         import_dm_annotations(dataset, task_data)

cvat/apps/dataset_manager/formats/labelme.py

@@ -4,23 +4,22 @@
 
 from tempfile import TemporaryDirectory
 
+from datumaro.components.dataset import Dataset
 from pyunpack import Archive
 
 from cvat.apps.dataset_manager.bindings import (CvatTaskDataExtractor,
     import_dm_annotations)
 from cvat.apps.dataset_manager.util import make_zip_archive
-from datumaro.components.project import Dataset
 
 from .registry import dm_env, exporter, importer
 
 
 @exporter(name='LabelMe', ext='ZIP', version='3.0')
 def _export(dst_file, task_data, save_images=False):
-    extractor = CvatTaskDataExtractor(task_data, include_images=save_images)
-    extractor = Dataset.from_extractors(extractor) # apply lazy transforms
+    dataset = Dataset.from_extractors(CvatTaskDataExtractor(
+        task_data, include_images=save_images), env=dm_env)
     with TemporaryDirectory() as temp_dir:
-        dm_env.converters.get('label_me').convert(extractor, save_dir=temp_dir,
-            save_images=save_images)
+        dataset.export(temp_dir, 'label_me', save_images=save_images)
 
         make_zip_archive(temp_dir, dst_file)
 
@@ -29,7 +28,6 @@ def _import(src_file, task_data):
     with TemporaryDirectory() as tmp_dir:
         Archive(src_file.name).extractall(tmp_dir)
 
-        dataset = dm_env.make_importer('label_me')(tmp_dir).make_dataset()
-        masks_to_polygons = dm_env.transforms.get('masks_to_polygons')
-        dataset = dataset.transform(masks_to_polygons)
+        dataset = Dataset.import_from(tmp_dir, 'label_me', env=dm_env)
+        dataset.transform('masks_to_polygons')
         import_dm_annotations(dataset, task_data)

cvat/apps/dataset_manager/formats/mask.py

@@ -4,12 +4,12 @@
 
 from tempfile import TemporaryDirectory
 
+from datumaro.components.dataset import Dataset
 from pyunpack import Archive
 
 from cvat.apps.dataset_manager.bindings import (CvatTaskDataExtractor,
     import_dm_annotations)
 from cvat.apps.dataset_manager.util import make_zip_archive
-from datumaro.components.project import Dataset
 
 from .registry import dm_env, exporter, importer
 from .utils import make_colormap
@@ -17,15 +17,13 @@ from .utils import make_colormap
 
 @exporter(name='Segmentation mask', ext='ZIP', version='1.1')
 def _export(dst_file, task_data, save_images=False):
-    extractor = CvatTaskDataExtractor(task_data, include_images=save_images)
-    envt = dm_env.transforms
-    extractor = extractor.transform(envt.get('polygons_to_masks'))
-    extractor = extractor.transform(envt.get('boxes_to_masks'))
-    extractor = extractor.transform(envt.get('merge_instance_segments'))
-    extractor = Dataset.from_extractors(extractor) # apply lazy transforms
+    dataset = Dataset.from_extractors(CvatTaskDataExtractor(
+        task_data, include_images=save_images), env=dm_env)
+    dataset.transform('polygons_to_masks')
+    dataset.transform('boxes_to_masks')
+    dataset.transform('merge_instance_segments')
     with TemporaryDirectory() as temp_dir:
-        dm_env.converters.get('voc_segmentation').convert(extractor,
-            save_dir=temp_dir, save_images=save_images,
+        dataset.export(temp_dir, 'voc_segmentation', save_images=save_images,
             apply_colormap=True, label_map=make_colormap(task_data))
 
         make_zip_archive(temp_dir, dst_file)
@@ -35,7 +33,6 @@ def _import(src_file, task_data):
     with TemporaryDirectory() as tmp_dir:
         Archive(src_file.name).extractall(tmp_dir)
 
-        dataset = dm_env.make_importer('voc')(tmp_dir).make_dataset()
-        masks_to_polygons = dm_env.transforms.get('masks_to_polygons')
-        dataset = dataset.transform(masks_to_polygons)
+        dataset = Dataset.import_from(tmp_dir, 'voc', env=dm_env)
+        dataset.transform('masks_to_polygons')
         import_dm_annotations(dataset, task_data)

cvat/apps/dataset_manager/formats/mot.py

@@ -4,23 +4,22 @@
 
 from tempfile import TemporaryDirectory
 
+import datumaro.components.extractor as datumaro
+from datumaro.components.dataset import Dataset
 from pyunpack import Archive
 
-import datumaro.components.extractor as datumaro
 from cvat.apps.dataset_manager.bindings import CvatTaskDataExtractor
 from cvat.apps.dataset_manager.util import make_zip_archive
-from datumaro.components.project import Dataset
 
 from .registry import dm_env, exporter, importer
 
 
 @exporter(name='MOT', ext='ZIP', version='1.1')
 def _export(dst_file, task_data, save_images=False):
-    extractor = CvatTaskDataExtractor(task_data, include_images=save_images)
-    extractor = Dataset.from_extractors(extractor) # apply lazy transforms
+    dataset = Dataset.from_extractors(CvatTaskDataExtractor(
+        task_data, include_images=save_images), env=dm_env)
     with TemporaryDirectory() as temp_dir:
-        dm_env.converters.get('mot_seq_gt').convert(extractor,
-            save_dir=temp_dir, save_images=save_images)
+        dataset.export(temp_dir, 'mot_seq_gt', save_images=save_images)
 
         make_zip_archive(temp_dir, dst_file)
 
@@ -29,7 +28,7 @@ def _import(src_file, task_data):
     with TemporaryDirectory() as tmp_dir:
         Archive(src_file.name).extractall(tmp_dir)
 
-        dataset = dm_env.make_importer('mot_seq')(tmp_dir).make_dataset()
+        dataset = Dataset.import_from(tmp_dir, 'mot_seq', env=dm_env)
 
         tracks = {}
         label_cat = dataset.categories()[datumaro.AnnotationType.label]

cvat/apps/dataset_manager/formats/mots.py

@@ -4,13 +4,13 @@
 
 from tempfile import TemporaryDirectory
 
+from datumaro.components.dataset import Dataset
+from datumaro.components.extractor import AnnotationType, Transform
 from pyunpack import Archive
 
 from cvat.apps.dataset_manager.bindings import (CvatTaskDataExtractor,
     find_dataset_root, match_dm_item)
 from cvat.apps.dataset_manager.util import make_zip_archive
-from datumaro.components.extractor import AnnotationType, Transform
-from datumaro.components.project import Dataset
 
 from .registry import dm_env, exporter, importer
 
@@ -22,16 +22,14 @@ class KeepTracks(Transform):
 
 @exporter(name='MOTS PNG', ext='ZIP', version='1.0')
 def _export(dst_file, task_data, save_images=False):
-    extractor = CvatTaskDataExtractor(task_data, include_images=save_images)
-    envt = dm_env.transforms
-    extractor = extractor.transform(KeepTracks) # can only export tracks
-    extractor = extractor.transform(envt.get('polygons_to_masks'))
-    extractor = extractor.transform(envt.get('boxes_to_masks'))
-    extractor = extractor.transform(envt.get('merge_instance_segments'))
-    extractor = Dataset.from_extractors(extractor) # apply lazy transforms
+    dataset = Dataset.from_extractors(CvatTaskDataExtractor(
+        task_data, include_images=save_images), env=dm_env)
+    dataset.transform(KeepTracks) # can only export tracks
+    dataset.transform('polygons_to_masks')
+    dataset.transform('boxes_to_masks')
+    dataset.transform('merge_instance_segments')
     with TemporaryDirectory() as temp_dir:
-        dm_env.converters.get('mots_png').convert(extractor,
-            save_dir=temp_dir, save_images=save_images)
+        dataset.export(temp_dir, 'mots_png', save_images=save_images)
 
         make_zip_archive(temp_dir, dst_file)
 
@@ -40,9 +38,8 @@ def _import(src_file, task_data):
     with TemporaryDirectory() as tmp_dir:
         Archive(src_file.name).extractall(tmp_dir)
 
-        dataset = dm_env.make_importer('mots')(tmp_dir).make_dataset()
-        masks_to_polygons = dm_env.transforms.get('masks_to_polygons')
-        dataset = dataset.transform(masks_to_polygons)
+        dataset = Dataset.import_from(tmp_dir, 'mots', env=dm_env)
+        dataset.transform('masks_to_polygons')
 
         tracks = {}
         label_cat = dataset.categories()[AnnotationType.label]

cvat/apps/dataset_manager/formats/pascal_voc.py

@@ -6,26 +6,25 @@ import os
 import os.path as osp
 import shutil
 from glob import glob
-
 from tempfile import TemporaryDirectory
 
+from datumaro.components.dataset import Dataset
 from pyunpack import Archive
 
 from cvat.apps.dataset_manager.bindings import (CvatTaskDataExtractor,
     import_dm_annotations)
 from cvat.apps.dataset_manager.util import make_zip_archive
-from datumaro.components.project import Dataset
 
 from .registry import dm_env, exporter, importer
 
 
 @exporter(name='PASCAL VOC', ext='ZIP', version='1.1')
 def _export(dst_file, task_data, save_images=False):
-    extractor = CvatTaskDataExtractor(task_data, include_images=save_images)
-    extractor = Dataset.from_extractors(extractor) # apply lazy transforms
+    dataset = Dataset.from_extractors(CvatTaskDataExtractor(
+        task_data, include_images=save_images), env=dm_env)
     with TemporaryDirectory() as temp_dir:
-        dm_env.converters.get('voc').convert(extractor,
-            save_dir=temp_dir, save_images=save_images, label_map='source')
+        dataset.export(temp_dir, 'voc', save_images=save_images,
+            label_map='source')
 
         make_zip_archive(temp_dir, dst_file)
 
@@ -56,7 +55,6 @@ def _import(src_file, task_data):
             for f in anno_files:
                 shutil.move(f, anno_dir)
 
-        dataset = dm_env.make_importer('voc')(tmp_dir).make_dataset()
-        masks_to_polygons = dm_env.transforms.get('masks_to_polygons')
-        dataset = dataset.transform(masks_to_polygons)
+        dataset = Dataset.import_from(tmp_dir, 'voc', env=dm_env)
+        dataset.transform('masks_to_polygons')
         import_dm_annotations(dataset, task_data)

cvat/apps/dataset_manager/formats/tfrecord.py

@@ -24,11 +24,10 @@ except ImportError:
 
 @exporter(name='TFRecord', ext='ZIP', version='1.0', enabled=tf_available)
 def _export(dst_file, task_data, save_images=False):
-    extractor = CvatTaskDataExtractor(task_data, include_images=save_images)
-    extractor = Dataset.from_extractors(extractor) # apply lazy transforms
+    dataset = Dataset.from_extractors(CvatTaskDataExtractor(
+        task_data, include_images=save_images), env=dm_env)
     with TemporaryDirectory() as temp_dir:
-        dm_env.converters.get('tf_detection_api').convert(extractor,
-            save_dir=temp_dir, save_images=save_images)
+        dataset.export(temp_dir, 'tf_detection_api', save_images=save_images)
 
         make_zip_archive(temp_dir, dst_file)
 
@@ -37,5 +36,5 @@ def _import(src_file, task_data):
     with TemporaryDirectory() as tmp_dir:
         Archive(src_file.name).extractall(tmp_dir)
 
-        dataset = dm_env.make_importer('tf_detection_api')(tmp_dir).make_dataset()
+        dataset = Dataset.import_from(tmp_dir, 'tf_detection_api', env=dm_env)
         import_dm_annotations(dataset, task_data)

cvat/apps/dataset_manager/formats/yolo.py

@@ -20,11 +20,10 @@ from .registry import dm_env, exporter, importer
 
 @exporter(name='YOLO', ext='ZIP', version='1.1')
 def _export(dst_file, task_data, save_images=False):
-    extractor = CvatTaskDataExtractor(task_data, include_images=save_images)
-    extractor = Dataset.from_extractors(extractor) # apply lazy transforms
+    dataset = Dataset.from_extractors(CvatTaskDataExtractor(
+        task_data, include_images=save_images), env=dm_env)
     with TemporaryDirectory() as temp_dir:
-        dm_env.converters.get('yolo').convert(extractor,
-            save_dir=temp_dir, save_images=save_images)
+        dataset.export(temp_dir, 'yolo', save_images=save_images)
 
         make_zip_archive(temp_dir, dst_file)
 
@@ -44,11 +43,11 @@ def _import(src_file, task_data):
                 frame_id = match_dm_item(DatasetItem(id=frame), task_data,
                     root_hint=root_hint)
                 frame_info = task_data.frame_info[frame_id]
-            except Exception:
+            except Exception: # nosec
                 pass
             if frame_info is not None:
                 image_info[frame] = (frame_info['height'], frame_info['width'])
 
-        dataset = dm_env.make_importer('yolo')(tmp_dir, image_info=image_info) \
-            .make_dataset()
+        dataset = Dataset.import_from(tmp_dir, 'yolo',
+            env=dm_env, image_info=image_info)
         import_dm_annotations(dataset, task_data)

cvat/apps/dataset_manager/tests/test_formats.py

@@ -357,17 +357,18 @@ class TaskExportTest(_DbTestBase):
                             project.config.remove('sources')
 
                             return project.make_dataset()
-                        return dm_env.make_importer(importer_name)(src) \
-                            .make_dataset()
+                        return datumaro.components.dataset. \
+                            Dataset.import_from(src, importer_name, env=dm_env)
 
                     if zipfile.is_zipfile(file_path):
                         with tempfile.TemporaryDirectory() as tmp_dir:
                             zipfile.ZipFile(file_path).extractall(tmp_dir)
                             dataset = load_dataset(tmp_dir)
+                            self.assertEqual(len(dataset), task["size"])
                     else:
                         dataset = load_dataset(file_path)
+                        self.assertEqual(len(dataset), task["size"])
 
-                    self.assertEqual(len(dataset), task["size"])
                 self._test_export(check, task, format_name, save_images=False)
 
     def test_can_skip_outside(self):

cvat/requirements/base.txt

@@ -45,5 +45,9 @@ tensorflow==2.4.1 # Optional requirement of Datumaro
 patool==1.12
 diskcache==5.0.2
 open3d==0.11.2
-# workaround for binary incompatibility with numpy when pycocotools is installed by wheel
-datumaro==0.1.5.1 --no-binary=datumaro --no-binary=pycocotools
+# --no-binary=datumaro: workaround for pip to install
+# opencv-headless instead of regular opencv, to actually run setup script
+# --no-binary=pycocotools: workaround for binary incompatibility on numpy 1.20
+# of pycocotools and tensorflow 2.4.1
+# when pycocotools is installed by wheel in python 3.8+
+datumaro==0.1.6.1 --no-binary=datumaro --no-binary=pycocotools