fix bug and change unittest mode

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10680402

modelscope/pipelines/cv/referring_video_object_segmentation_pipeline.py

@@ -138,6 +138,19 @@ class ReferringVideoObjectSegmentationPipeline(Pipeline):
             video_np = rearrange(self.video,
                                  't c h w -> t h w c').numpy() / 255.0
 
+            # set font for text query in output video
+            if self.model.cfg.pipeline.output_font:
+                try:
+                    font = ImageFont.truetype(
+                        font=self.model.cfg.pipeline.output_font,
+                        size=self.model.cfg.pipeline.output_font_size)
+                except OSError:
+                    logger.error('can\'t open resource %s, load default font'
+                                 % self.model.cfg.pipeline.output_font)
+                    font = ImageFont.load_default()
+            else:
+                font = ImageFont.load_default()
+
             # del video
             pred_masks_per_frame = rearrange(
                 torch.stack(inputs), 'q t 1 h w -> t q h w').numpy()
@@ -158,12 +171,6 @@ class ReferringVideoObjectSegmentationPipeline(Pipeline):
                 W, H = vid_frame.size
                 draw = ImageDraw.Draw(vid_frame)
 
-                if self.model.cfg.pipeline.output_font:
-                    font = ImageFont.truetype(
-                        font=self.model.cfg.pipeline.output_font,
-                        size=self.model.cfg.pipeline.output_font_size)
-                else:
-                    font = ImageFont.load_default()
                 for i, (text_query, color) in enumerate(
                         zip(self.text_queries, colors), start=1):
                     w, h = draw.textsize(text_query, font=font)
@@ -173,9 +180,6 @@ class ReferringVideoObjectSegmentationPipeline(Pipeline):
                               fill=tuple(color) + (255, ),
                               font=font)
                 masked_video.append(np.array(vid_frame))
-            print(type(vid_frame))
-            print(type(masked_video[0]))
-            print(masked_video[0].shape)
             # generate and save the output clip:
 
             assert self.model.cfg.pipeline.output_path

tests/pipelines/test_referring_video_object_segmentation.py

@@ -14,7 +14,7 @@ class ReferringVideoObjectSegmentationTest(unittest.TestCase,
         self.task = Tasks.referring_video_object_segmentation
         self.model_id = 'damo/cv_swin-t_referring_video-object-segmentation'
 
-    @unittest.skip('skip since the model is set to private for now')
+    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
     def test_referring_video_object_segmentation(self):
         input_location = 'data/test/videos/referring_video_object_segmentation_test_video.mp4'
         text_queries = [
@@ -31,7 +31,7 @@ class ReferringVideoObjectSegmentationTest(unittest.TestCase,
         else:
             raise ValueError('process error')
 
-    @unittest.skip('skip since the model is set to private for now')
+    @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
     def test_referring_video_object_segmentation_with_default_task(self):
         input_location = 'data/test/videos/referring_video_object_segmentation_test_video.mp4'
         text_queries = [

tests/trainers/test_referring_video_object_segmentation_trainer.py

@@ -7,8 +7,8 @@ import zipfile
 
 from modelscope.hub.snapshot_download import snapshot_download
 from modelscope.metainfo import Trainers
-from modelscope.models.cv.movie_scene_segmentation import \
-    MovieSceneSegmentationModel
+from modelscope.models.cv.referring_video_object_segmentation import \
+    ReferringVideoObjectSegmentation
 from modelscope.msdatasets import MsDataset
 from modelscope.trainers import build_trainer
 from modelscope.utils.config import Config, ConfigDict
@@ -46,7 +46,6 @@ class TestImageInstanceSegmentationTrainer(unittest.TestCase):
             dataset_name=train_data_cfg.name,
             split=train_data_cfg.split,
             cfg=train_data_cfg.cfg,
-            namespace='damo',
             test_mode=train_data_cfg.test_mode)
         assert next(
             iter(self.train_dataset.config_kwargs['split_config'].values()))
@@ -55,14 +54,13 @@ class TestImageInstanceSegmentationTrainer(unittest.TestCase):
             dataset_name=test_data_cfg.name,
             split=test_data_cfg.split,
             cfg=test_data_cfg.cfg,
-            namespace='damo',
             test_mode=test_data_cfg.test_mode)
         assert next(
             iter(self.test_dataset.config_kwargs['split_config'].values()))
 
         self.max_epochs = max_epochs
 
-    @unittest.skip('skip since the model is set to private for now')
+    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
     def test_trainer(self):
         kwargs = dict(
             model=self.model_id,
@@ -77,11 +75,11 @@ class TestImageInstanceSegmentationTrainer(unittest.TestCase):
         results_files = os.listdir(trainer.work_dir)
         self.assertIn(f'{trainer.timestamp}.log.json', results_files)
 
-    @unittest.skip('skip since the model is set to private for now')
+    @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
     def test_trainer_with_model_and_args(self):
 
         cache_path = snapshot_download(self.model_id)
-        model = MovieSceneSegmentationModel.from_pretrained(cache_path)
+        model = ReferringVideoObjectSegmentation.from_pretrained(cache_path)
         kwargs = dict(
             cfg_file=os.path.join(cache_path, ModelFile.CONFIGURATION),
             model=model,