[to #42322933] fix typo

modelscope/outputs.py

@@ -128,11 +128,10 @@ TASK_OUTPUTS = {
     #   "output_img": np.array with shape(h, w, 4)
     # }
     Tasks.image_matting: [OutputKeys.OUTPUT_IMG],
-    Tasks.protrait_matting: [OutputKeys.OUTPUT_IMG],
+    Tasks.portrait_matting: [OutputKeys.OUTPUT_IMG],
 
     # image editing task result for a single image
     # {"output_img": np.array with shape (h, w, 3)}
-    Tasks.image_protrait_enhancement: [OutputKeys.OUTPUT_IMG],
     Tasks.skin_retouching: [OutputKeys.OUTPUT_IMG],
     Tasks.image_super_resolution: [OutputKeys.OUTPUT_IMG],
     Tasks.image_colorization: [OutputKeys.OUTPUT_IMG],

modelscope/pipelines/cv/image_matting_pipeline.py

@@ -16,7 +16,7 @@ logger = get_logger()
 
 
 @PIPELINES.register_module(
-    Tasks.protrait_matting, module_name=Pipelines.image_matting)
+    Tasks.portrait_matting, module_name=Pipelines.image_matting)
 @PIPELINES.register_module(
     Tasks.image_matting, module_name=Pipelines.image_matting)
 class ImageMattingPipeline(Pipeline):

modelscope/utils/constant.py

@@ -5,8 +5,6 @@ import enum
 class Fields(object):
     """ Names for different application fields
     """
-    # image = 'image'
-    # video = 'video'
     cv = 'cv'
     nlp = 'nlp'
     audio = 'audio'
@@ -34,10 +32,9 @@ class CVTasks(object):
 
     image_segmentation = 'image-segmentation'
     image_matting = 'image-matting'
-    protrait_matting = 'protrait-matting'
+    portrait_matting = 'portrait-matting'
 
-    # image editting
-    image_protrait_enhancement = 'image-protrait-enhancement'
+    # image editing
     skin_retouching = 'skin-retouching'
     image_super_resolution = 'image-super-resolution'
     image_colorization = 'image-colorization'

tests/pipelines/test_image_matting.py

@@ -26,7 +26,7 @@ class ImageMattingTest(unittest.TestCase):
             model_file = osp.join(tmp_dir, ModelFile.TF_GRAPH_FILE)
             with open(model_file, 'wb') as ofile:
                 ofile.write(File.read(model_path))
-            img_matting = pipeline(Tasks.protrait_matting, model=tmp_dir)
+            img_matting = pipeline(Tasks.portrait_matting, model=tmp_dir)
 
             result = img_matting('data/test/images/image_matting.png')
             cv2.imwrite('result.png', result[OutputKeys.OUTPUT_IMG])
@@ -38,7 +38,7 @@ class ImageMattingTest(unittest.TestCase):
         # input_location = '/dir/to/images'
 
         dataset = MsDataset.load(input_location, target='image')
-        img_matting = pipeline(Tasks.protrait_matting, model=self.model_id)
+        img_matting = pipeline(Tasks.portrait_matting, model=self.model_id)
         # note that for dataset output, the inference-output is a Generator that can be iterated.
         result = img_matting(dataset)
         cv2.imwrite('result.png', next(result)[OutputKeys.OUTPUT_IMG])
@@ -46,7 +46,7 @@ class ImageMattingTest(unittest.TestCase):
 
     @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
     def test_run_modelhub(self):
-        img_matting = pipeline(Tasks.protrait_matting, model=self.model_id)
+        img_matting = pipeline(Tasks.portrait_matting, model=self.model_id)
 
         result = img_matting('data/test/images/image_matting.png')
         cv2.imwrite('result.png', result[OutputKeys.OUTPUT_IMG])
@@ -54,7 +54,7 @@ class ImageMattingTest(unittest.TestCase):
 
     @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
     def test_run_modelhub_default_model(self):
-        img_matting = pipeline(Tasks.protrait_matting)
+        img_matting = pipeline(Tasks.portrait_matting)
 
         result = img_matting('data/test/images/image_matting.png')
         cv2.imwrite('result.png', result[OutputKeys.OUTPUT_IMG])
@@ -67,7 +67,7 @@ class ImageMattingTest(unittest.TestCase):
             namespace='damotest',
             split='test',
             target='file')
-        img_matting = pipeline(Tasks.protrait_matting, model=self.model_id)
+        img_matting = pipeline(Tasks.portrait_matting, model=self.model_id)
         result = img_matting(dataset)
         for i in range(2):
             cv2.imwrite(f'result_{i}.png', next(result)[OutputKeys.OUTPUT_IMG])