[to #42322933] add universal_matting pipeline

上线通用抠图模型

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/11505692

data/test/images/universal_matting.jpg

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:78d7bf999d1a4186309693ff1b966edb3ccd40f7861a7589167cf9e33897a693
+size 369725

modelscope/metainfo.py

@@ -207,6 +207,7 @@ class Pipelines(object):
     """
     # vision tasks
     portrait_matting = 'unet-image-matting'
+    universal_matting = 'unet-universal-matting'
     image_denoise = 'nafnet-image-denoise'
     image_deblur = 'nafnet-image-deblur'
     person_image_cartoon = 'unet-person-image-cartoon'
@@ -461,6 +462,8 @@ DEFAULT_MODEL_FOR_PIPELINE = {
      ),  # TODO: revise back after passing the pr
     Tasks.portrait_matting: (Pipelines.portrait_matting,
                              'damo/cv_unet_image-matting'),
+    Tasks.universal_matting: (Pipelines.universal_matting,
+                              'damo/cv_unet_universal-matting'),
     Tasks.human_detection: (Pipelines.human_detection,
                             'damo/cv_resnet18_human-detection'),
     Tasks.image_object_detection: (Pipelines.object_detection,

modelscope/outputs/outputs.py

@@ -314,6 +314,7 @@ TASK_OUTPUTS = {
     #                 , shape(h, w) for crowd counting
     # }
     Tasks.portrait_matting: [OutputKeys.OUTPUT_IMG],
+    Tasks.universal_matting: [OutputKeys.OUTPUT_IMG],
 
     # image_quality_assessment_mos result for a single image is a score in range [0, 1]
     # {0.5}

modelscope/pipelines/cv/image_matting_pipeline.py

@@ -4,6 +4,7 @@ from typing import Any, Dict
 
 import cv2
 import numpy as np
+import tensorflow as tf
 
 from modelscope.metainfo import Pipelines
 from modelscope.outputs import OutputKeys
@@ -14,11 +15,16 @@ from modelscope.utils.constant import ModelFile, Tasks
 from modelscope.utils.device import device_placement
 from modelscope.utils.logger import get_logger
 
+if tf.__version__ >= '2.0':
+    tf = tf.compat.v1
+
 logger = get_logger()
 
 
 @PIPELINES.register_module(
     Tasks.portrait_matting, module_name=Pipelines.portrait_matting)
+@PIPELINES.register_module(
+    Tasks.universal_matting, module_name=Pipelines.universal_matting)
 class ImageMattingPipeline(Pipeline):
 
     def __init__(self, model: str, **kwargs):
@@ -28,9 +34,6 @@ class ImageMattingPipeline(Pipeline):
             model: model id on modelscope hub.
         """
         super().__init__(model=model, **kwargs)
-        import tensorflow as tf
-        if tf.__version__ >= '2.0':
-            tf = tf.compat.v1
         model_path = osp.join(self.model, ModelFile.TF_GRAPH_FILE)
 
         with device_placement(self.framework, self.device_name):

modelscope/utils/constant.py

@@ -55,6 +55,7 @@ class CVTasks(object):
     video_depth_estimation = 'video-depth-estimation'
     panorama_depth_estimation = 'panorama-depth-estimation'
     portrait_matting = 'portrait-matting'
+    universal_matting = 'universal-matting'
     text_driven_segmentation = 'text-driven-segmentation'
     shop_segmentation = 'shop-segmentation'
     hand_static = 'hand-static'

tests/pipelines/test_universal_matting.py

@@ -0,0 +1,44 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+import os.path as osp
+import unittest
+
+import cv2
+
+from modelscope.msdatasets import MsDataset
+from modelscope.outputs import OutputKeys
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import ModelFile, Tasks
+from modelscope.utils.demo_utils import DemoCompatibilityCheck
+from modelscope.utils.test_utils import test_level
+
+
+class UniversalMattingTest(unittest.TestCase, DemoCompatibilityCheck):
+
+    def setUp(self) -> None:
+        self.model_id = 'damo/cv_unet_universal-matting'
+
+    @unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
+    def test_run_with_dataset(self):
+        input_location = ['data/test/images/universal_matting.jpg']
+
+        dataset = MsDataset.load(input_location, target='image')
+        img_matting = pipeline(Tasks.universal_matting, model=self.model_id)
+        result = img_matting(dataset)
+        cv2.imwrite('result.png', next(result)[OutputKeys.OUTPUT_IMG])
+        print(f'Output written to {osp.abspath("result.png")}')
+
+    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
+    def test_run_modelhub(self):
+        img_matting = pipeline(Tasks.universal_matting, model=self.model_id)
+
+        result = img_matting('data/test/images/universal_matting.jpg')
+        cv2.imwrite('result.png', result[OutputKeys.OUTPUT_IMG])
+        print(f'Output written to {osp.abspath("result.png")}')
+
+    @unittest.skip('demo compatibility test is only enabled on a needed-basis')
+    def test_demo_compatibility(self):
+        self.compatibility_check()
+
+
+if __name__ == '__main__':
+    unittest.main()