[to #42322933]add semantic-segmentation task output is numpy mask for demo-service

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10265856

modelscope/models/cv/salient_detection/salient_model.py

@@ -14,7 +14,8 @@ from modelscope.utils.constant import ModelFile, Tasks
 from .models import U2NET
 
 
-@MODELS.register_module(Tasks.image_segmentation, module_name=Models.detection)
+@MODELS.register_module(
+    Tasks.semantic_segmentation, module_name=Models.detection)
 class SalientDetection(TorchModel):
 
     def __init__(self, model_dir: str, *args, **kwargs):

modelscope/outputs.py

@@ -151,6 +151,12 @@ TASK_OUTPUTS = {
     Tasks.image_segmentation:
     [OutputKeys.SCORES, OutputKeys.LABELS, OutputKeys.MASKS],
 
+    # semantic segmentation result for single sample
+    #   {
+    #       "masks": [np.array # 2D array containing only 0, 255]
+    #   }
+    Tasks.semantic_segmentation: [OutputKeys.MASKS],
+
     # image matting result for single sample
     # {
     #   "output_img": np.array with shape(h, w, 4)

modelscope/pipelines/cv/image_salient_detection_pipeline.py

@@ -9,7 +9,7 @@ from modelscope.utils.constant import Tasks
 
 
 @PIPELINES.register_module(
-    Tasks.image_segmentation, module_name=Pipelines.salient_detection)
+    Tasks.semantic_segmentation, module_name=Pipelines.salient_detection)
 class ImageSalientDetectionPipeline(Pipeline):
 
     def __init__(self, model: str, **kwargs):
@@ -39,9 +39,5 @@ class ImageSalientDetectionPipeline(Pipeline):
     def postprocess(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
 
         data = self.model.postprocess(inputs)
-        outputs = {
-            OutputKeys.SCORES: None,
-            OutputKeys.LABELS: None,
-            OutputKeys.MASKS: data
-        }
+        outputs = {OutputKeys.MASKS: data}
         return outputs

modelscope/utils/constant.py

@@ -38,6 +38,7 @@ class CVTasks(object):
     image_object_detection = 'image-object-detection'
 
     image_segmentation = 'image-segmentation'
+    semantic_segmentation = 'semantic-segmentation'
     portrait_matting = 'portrait-matting'
     text_driven_segmentation = 'text-driven-segmentation'
     shop_segmentation = 'shop-segmentation'

tests/pipelines/test_salient_detection.py

@@ -11,17 +11,16 @@ from modelscope.utils.test_utils import test_level
 class SalientDetectionTest(unittest.TestCase, DemoCompatibilityCheck):
 
     def setUp(self) -> None:
-        self.task = Tasks.image_segmentation
+        self.task = Tasks.semantic_segmentation
         self.model_id = 'damo/cv_u2net_salient-detection'
 
     @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
     def test_salient_detection(self):
         input_location = 'data/test/images/image_salient_detection.jpg'
         model_id = 'damo/cv_u2net_salient-detection'
-        salient_detect = pipeline(Tasks.image_segmentation, model=model_id)
+        salient_detect = pipeline(Tasks.semantic_segmentation, model=model_id)
         result = salient_detect(input_location)
         import cv2
-        # result[OutputKeys.MASKS] is salient map result,other keys are not used
         cv2.imwrite(input_location + '_salient.jpg', result[OutputKeys.MASKS])
 
     @unittest.skip('demo compatibility test is only enabled on a needed-basis')