[feature] Visualizer compatible with MultiTaskDataSample

mmpretrain/visualization/visualizer.py

@@ -11,7 +11,7 @@
 from mmengine.visualization.utils import img_from_canvas
 
 from mmpretrain.registry import VISUALIZERS
-from mmpretrain.structures import DataSample
+from mmpretrain.structures import DataSample, MultiTaskDataSample
 from .utils import create_figure, get_adaptive_scale
 
 
@@ -99,6 +99,67 @@ def visualize_cls(self,
         Returns:
             np.ndarray: The visualization image.
         """
+
+        def _draw_gt(data_sample: DataSample,
+                     classes: Optional[Sequence[str]],
+                     draw_gt: bool,
+                     texts: Sequence[str],
+                     parent_task: str = ''):
+            if isinstance(data_sample, MultiTaskDataSample):
+                for task in data_sample.tasks:
+                    sub_task = f'{parent_task}_{task}' if parent_task else task
+                    _draw_gt(
+                        data_sample.get(task), classes, draw_gt, texts,
+                        sub_task)
+            else:
+                if draw_gt and 'gt_label' in data_sample:
+                    idx = data_sample.gt_label.tolist()
+                    class_labels = [''] * len(idx)
+                    if classes is not None:
+                        class_labels = [f' ({classes[i]})' for i in idx]
+                    labels = [
+                        str(idx[i]) + class_labels[i] for i in range(len(idx))
+                    ]
+                    prefix = f'{parent_task} Ground truth: ' if parent_task \
+                        else 'Ground truth: '
+                    texts.append(prefix +
+                                 ('\n' + ' ' * len(prefix)).join(labels))
+
+        def _draw_pred(data_sample: DataSample,
+                       classes: Optional[Sequence[str]],
+                       draw_pred: bool,
+                       draw_score: bool,
+                       texts: Sequence[str],
+                       parent_task: str = ''):
+            if isinstance(data_sample, MultiTaskDataSample):
+                for task in data_sample.tasks:
+                    sub_task = f'{parent_task}_{task}' if parent_task else task
+                    _draw_pred(
+                        data_sample.get(task), classes, draw_pred, draw_score,
+                        texts, sub_task)
+            else:
+                if draw_pred and 'pred_label' in data_sample:
+                    idx = data_sample.pred_label.tolist()
+                    score_labels = [''] * len(idx)
+                    class_labels = [''] * len(idx)
+                    if draw_score and 'pred_score' in data_sample:
+                        score_labels = [
+                            f', {data_sample.pred_score[i].item():.2f}'
+                            for i in idx
+                        ]
+
+                    if classes is not None:
+                        class_labels = [f' ({classes[i]})' for i in idx]
+
+                    labels = [
+                        str(idx[i]) + score_labels[i] + class_labels[i]
+                        for i in range(len(idx))
+                    ]
+                    prefix = f'{parent_task} Prediction: ' if parent_task \
+                        else 'Prediction: '
+                    texts.append(prefix +
+                                 ('\n' + ' ' * len(prefix)).join(labels))
+
         if self.dataset_meta is not None:
             classes = classes or self.dataset_meta.get('classes', None)
 
@@ -114,33 +175,9 @@ def visualize_cls(self,
         texts = []
         self.set_image(image)
 
-        if draw_gt and 'gt_label' in data_sample:
-            idx = data_sample.gt_label.tolist()
-            class_labels = [''] * len(idx)
-            if classes is not None:
-                class_labels = [f' ({classes[i]})' for i in idx]
-            labels = [str(idx[i]) + class_labels[i] for i in range(len(idx))]
-            prefix = 'Ground truth: '
-            texts.append(prefix + ('\n' + ' ' * len(prefix)).join(labels))
-
-        if draw_pred and 'pred_label' in data_sample:
-            idx = data_sample.pred_label.tolist()
-            score_labels = [''] * len(idx)
-            class_labels = [''] * len(idx)
-            if draw_score and 'pred_score' in data_sample:
-                score_labels = [
-                    f', {data_sample.pred_score[i].item():.2f}' for i in idx
-                ]
-
-            if classes is not None:
-                class_labels = [f' ({classes[i]})' for i in idx]
-
-            labels = [
-                str(idx[i]) + score_labels[i] + class_labels[i]
-                for i in range(len(idx))
-            ]
-            prefix = 'Prediction: '
-            texts.append(prefix + ('\n' + ' ' * len(prefix)).join(labels))
+        _draw_gt(data_sample, classes, draw_gt, texts)
+
+        _draw_pred(data_sample, classes, draw_pred, draw_score, texts)
 
         img_scale = get_adaptive_scale(image.shape[:2])
         text_cfg = {

tests/test_visualization/test_visualizer.py

@@ -7,7 +7,7 @@
 import numpy as np
 import torch
 
-from mmpretrain.structures import DataSample
+from mmpretrain.structures import DataSample, MultiTaskDataSample
 from mmpretrain.visualization import UniversalVisualizer
 
 
@@ -123,6 +123,46 @@ def draw_texts(text, font_sizes, *_, **__):
                 data_sample,
                 rescale_factor=2.)
 
+    def test_visualize_multitask_cls(self):
+        image = np.ones((1000, 1000, 3), np.uint8)
+        gt_label = {'task0': {'task00': 2, 'task01': 1}, 'task1': 1}
+        data_sample = MultiTaskDataSample()
+        task_sample = DataSample().set_gt_label(
+            gt_label['task1']).set_pred_label(1).set_pred_score(
+                torch.tensor([0.1, 0.8, 0.1]))
+        data_sample.set_field(task_sample, 'task1')
+        data_sample.set_field(MultiTaskDataSample(), 'task0')
+        for task_name in gt_label['task0']:
+            task_sample = DataSample().set_gt_label(
+                gt_label['task0'][task_name]).set_pred_label(2).set_pred_score(
+                    torch.tensor([0.1, 0.4, 0.5]))
+            data_sample.task0.set_field(task_sample, task_name)
+
+        # Test show
+        def mock_show(drawn_img, win_name, wait_time):
+            self.assertFalse((image == drawn_img).all())
+            self.assertEqual(win_name, 'test_cls')
+            self.assertEqual(wait_time, 0)
+
+        with patch.object(self.vis, 'show', mock_show):
+            self.vis.visualize_cls(
+                image=image,
+                data_sample=data_sample,
+                show=True,
+                name='test_cls',
+                step=2)
+
+        # Test storage backend.
+        save_file = osp.join(self.tmpdir.name,
+                             'vis_data/vis_image/test_cls_2.png')
+        self.assertTrue(osp.exists(save_file))
+
+        # Test out_file
+        out_file = osp.join(self.tmpdir.name, 'results_2.png')
+        self.vis.visualize_cls(
+            image=image, data_sample=data_sample, out_file=out_file)
+        self.assertTrue(osp.exists(out_file))
+
     def test_visualize_image_retrieval(self):
         image = np.ones((10, 10, 3), np.uint8)
         data_sample = DataSample().set_pred_score([0.1, 0.8, 0.1])