Fix Datumaro visualizer's import errors after introducing lazy import

CHANGELOG.md

@@ -25,6 +25,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
   (<https://github.com/openvinotoolkit/datumaro/pull/1210>)
 - Fix label compare of distance method
   (<https://github.com/openvinotoolkit/datumaro/pull/1205>)
+- Fix Datumaro visualizer's import errors after introducing lazy import
+  (<https://github.com/openvinotoolkit/datumaro/pull/1220>)
 - Fix broken link to supported formats in readme
   (<https://github.com/openvinotoolkit/datumaro/pull/1221>)
 

src/datumaro/cli/util/compare.py

@@ -21,14 +21,11 @@
 from datumaro.util.import_util import lazy_import
 
 if TYPE_CHECKING:
-    import matplotlib.pyplot as plt
-
     with warnings.catch_warnings():
         warnings.simplefilter("ignore")
         import tensorboardX as tb
 else:
     tb = lazy_import("tensorboardX")
-    plt = lazy_import("matplotlib.pyplot")
 
 
 class DistanceCompareVisualizer:
@@ -292,6 +289,8 @@ def save_as_tensorboard(self, img, name):
         self._file_writer.add_image(name, img)
 
     def save_conf_matrix(self, conf_matrix, filename):
+        import matplotlib.pyplot as plt
+
         def _get_class_map(label_categories):
             classes = None
             if label_categories is not None:

src/datumaro/components/algorithms/noisy_label_detection/loss_dynamics_analyzer.py

@@ -1,6 +1,7 @@
 # Copyright (C) 2023 Intel Corporation
 #
 # SPDX-License-Identifier: MIT
+from __future__ import annotations
 
 from collections import defaultdict
 from dataclasses import dataclass, field
@@ -13,8 +14,6 @@
 if TYPE_CHECKING:
     from matplotlib.figure import Figure
     from pandas import DataFrame, Series
-else:
-    DataFrame, Series, Figure = None, None, None
 
 
 __all__ = ["LossDynamicsAnalyzer", "NoisyLabelCandidate"]

src/datumaro/components/visualizer.py

@@ -1,6 +1,8 @@
 # Copyright (C) 2023 Intel Corporation
 #
 # SPDX-License-Identifier: MIT
+from __future__ import annotations
+
 import logging as log
 import math
 import random
@@ -35,10 +37,6 @@
     from matplotlib.axes import Axes
     from matplotlib.figure import Figure
     from matplotlib.text import Text
-else:
-    Figure = None
-    Axes = None
-    Text = None
 
 CAPTION_BBOX_PAD = 0.2
 DEFAULT_COLOR_CYCLES: List[str] = [

tests/unit/test_compare.py

@@ -1,10 +1,16 @@
+# Copyright (C) 2020-2023 Intel Corporation
+#
+# SPDX-License-Identifier: MIT
+import os
 import platform
 import unittest
 from unittest import TestCase, skipIf
-from unittest.mock import call, mock_open, patch
+from unittest.mock import MagicMock, call, mock_open, patch
 
 import numpy as np
+import pytest
 
+from datumaro.cli.util.compare import DistanceCompareVisualizer
 from datumaro.components.annotation import Bbox, Caption, Label, Mask, Points
 from datumaro.components.comparator import DistanceComparator, EqualityComparator, TableComparator
 from datumaro.components.dataset_base import DEFAULT_SUBSET_NAME, DatasetItem
@@ -462,3 +468,40 @@ def test_save_compare_report(
                 call(f"Low-level Comparison:\n{mock_low_level_table}\n\n"),
             ]
         )
+
+
+class DistanceCompareVisualizerTest:
+    @pytest.fixture
+    def fxt_dataset_pair(self):
+        """Two datasets pair which has the same (id, subset) dataset item to be compared."""
+        class_count = 3
+        dataset_1 = Dataset.from_iterable(
+            [DatasetItem(id=1, annotations=[Label(label=idx) for idx in range(class_count)])],
+            categories=[f"label_{idx}" for idx in range(class_count)],
+        )
+        dataset_2 = Dataset.from_iterable(
+            [DatasetItem(id=1, annotations=[Label(label=idx) for idx in range(class_count)])],
+            categories=[f"label_{idx}" for idx in range(class_count)],
+        )
+        return dataset_1, dataset_2
+
+    @pytest.mark.parametrize("output_format", ["simple", "tensorboard"])
+    def test_save(self, fxt_dataset_pair, test_dir, output_format):
+        mock_dist_comparator = MagicMock(spec=DistanceComparator)
+
+        # matches, a_unmatched, b_unmatched = label_diff
+        mock_dist_comparator.match_labels.return_value = ([0], [1], [2])
+        mock_dist_comparator.match_boxes.return_value = ([], [], [], [])
+        mock_dist_comparator.match_polygons.return_value = ([], [], [], [])
+        mock_dist_comparator.match_masks.return_value = ([], [], [], [])
+
+        with DistanceCompareVisualizer(
+            save_dir=test_dir,
+            comparator=mock_dist_comparator,
+            output_format=output_format,
+        ) as visualizer:
+            first_dataset, second_dataset = fxt_dataset_pair
+            visualizer.save(first_dataset, second_dataset)
+
+            # Assert non-empty after save()
+            assert os.listdir(test_dir)