[to #42322933] add human whole body model and image object detection auto model

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10319306

data/test/images/auto_demo.jpg

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:76bf84536edbaf192a8a699efc62ba2b06056bac12c426ecfcc2e003d91fbd32
+size 53219

data/test/images/keypoints_detect/body_keypoints_detection.jpg

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:379e11d7fc3734d3ec95afd0d86460b4653fbf4bb1f57f993610d6a6fd30fd3d
-size 1702339

data/test/images/keypoints_detect/img_test_wholebody.jpg

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dec0fbb931cb609bf481e56b89cd2fbbab79839f22832c3bbe69a8fae2769cdd
+size 167407

modelscope/metainfo.py

@@ -40,6 +40,7 @@ class Models(object):
     mtcnn = 'mtcnn'
     ulfd = 'ulfd'
     video_inpainting = 'video-inpainting'
+    human_wholebody_keypoint = 'human-wholebody-keypoint'
     hand_static = 'hand-static'
     face_human_hand_detection = 'face-human-hand-detection'
     face_emotion = 'face-emotion'
@@ -49,6 +50,7 @@ class Models(object):
     # EasyCV models
     yolox = 'YOLOX'
     segformer = 'Segformer'
+    image_object_detection_auto = 'image-object-detection-auto'
 
     # nlp models
     bert = 'bert'
@@ -170,6 +172,7 @@ class Pipelines(object):
     ocr_recognition = 'convnextTiny-ocr-recognition'
     image_portrait_enhancement = 'gpen-image-portrait-enhancement'
     image_to_image_generation = 'image-to-image-generation'
+    image_object_detection_auto = 'yolox_image-object-detection-auto'
     skin_retouching = 'unet-skin-retouching'
     tinynas_classification = 'tinynas-classification'
     tinynas_detection = 'tinynas-detection'
@@ -185,6 +188,7 @@ class Pipelines(object):
     movie_scene_segmentation = 'resnet50-bert-movie-scene-segmentation'
     shop_segmentation = 'shop-segmentation'
     video_inpainting = 'video-inpainting'
+    human_wholebody_keypoint = 'hrnetw48_human-wholebody-keypoint_image'
     pst_action_recognition = 'patchshift-action-recognition'
     hand_static = 'hand-static'
     face_human_hand_detection = 'face-human-hand-detection'
@@ -427,6 +431,7 @@ class Datasets(object):
     """
     ClsDataset = 'ClsDataset'
     Face2dKeypointsDataset = 'Face2dKeypointsDataset'
+    HumanWholeBodyKeypointDataset = 'HumanWholeBodyKeypointDataset'
     SegDataset = 'SegDataset'
     DetDataset = 'DetDataset'
     DetImagesMixDataset = 'DetImagesMixDataset'

modelscope/models/cv/__init__.py

@@ -4,15 +4,15 @@
 from . import (action_recognition, animal_recognition, body_2d_keypoints,
                body_3d_keypoints, cartoon, cmdssl_video_embedding,
                crowd_counting, face_2d_keypoints, face_detection,
-               face_generation, image_classification, image_color_enhance,
-               image_colorization, image_denoise, image_inpainting,
-               image_instance_segmentation, image_panoptic_segmentation,
-               image_portrait_enhancement, image_reid_person,
-               image_semantic_segmentation, image_to_image_generation,
-               image_to_image_translation, movie_scene_segmentation,
-               object_detection, product_retrieval_embedding,
-               realtime_object_detection, salient_detection, shop_segmentation,
-               super_resolution, video_single_object_tracking,
-               video_summarization, virual_tryon)
+               face_generation, human_wholebody_keypoint, image_classification,
+               image_color_enhance, image_colorization, image_denoise,
+               image_inpainting, image_instance_segmentation,
+               image_panoptic_segmentation, image_portrait_enhancement,
+               image_reid_person, image_semantic_segmentation,
+               image_to_image_generation, image_to_image_translation,
+               movie_scene_segmentation, object_detection,
+               product_retrieval_embedding, realtime_object_detection,
+               salient_detection, shop_segmentation, super_resolution,
+               video_single_object_tracking, video_summarization, virual_tryon)
 
 # yapf: enable

modelscope/models/cv/human_wholebody_keypoint/__init__.py

@@ -0,0 +1,22 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+from typing import TYPE_CHECKING
+
+from modelscope.utils.import_utils import LazyImportModule
+
+if TYPE_CHECKING:
+    from .human_wholebody_keypoint import HumanWholeBodyKeypoint
+
+else:
+    _import_structure = {
+        'human_wholebody_keypoint': ['HumanWholeBodyKeypoint']
+    }
+
+    import sys
+
+    sys.modules[__name__] = LazyImportModule(
+        __name__,
+        globals()['__file__'],
+        _import_structure,
+        module_spec=__spec__,
+        extra_objects={},
+    )

modelscope/models/cv/human_wholebody_keypoint/human_wholebody_keypoint.py

@@ -0,0 +1,17 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+from easycv.models.pose.top_down import TopDown
+
+from modelscope.metainfo import Models
+from modelscope.models.builder import MODELS
+from modelscope.models.cv.easycv_base import EasyCVBaseModel
+from modelscope.utils.constant import Tasks
+
+
+@MODELS.register_module(
+    group_key=Tasks.human_wholebody_keypoint,
+    module_name=Models.human_wholebody_keypoint)
+class HumanWholeBodyKeypoint(EasyCVBaseModel, TopDown):
+
+    def __init__(self, model_dir=None, *args, **kwargs):
+        EasyCVBaseModel.__init__(self, model_dir, args, kwargs)
+        TopDown.__init__(self, *args, **kwargs)

modelscope/models/cv/object_detection/__init__.py

@@ -10,7 +10,7 @@ if TYPE_CHECKING:
 else:
     _import_structure = {
         'mmdet_model': ['DetectionModel'],
-        'yolox_pai': ['YOLOX']
+        'yolox_pai': ['YOLOX'],
     }
 
     import sys

modelscope/models/cv/object_detection/yolox_pai.py

@@ -9,6 +9,9 @@ from modelscope.utils.constant import Tasks
 
 @MODELS.register_module(
     group_key=Tasks.image_object_detection, module_name=Models.yolox)
+@MODELS.register_module(
+    group_key=Tasks.image_object_detection,
+    module_name=Models.image_object_detection_auto)
 class YOLOX(EasyCVBaseModel, _YOLOX):
 
     def __init__(self, model_dir=None, *args, **kwargs):

modelscope/msdatasets/cv/human_wholebody_keypoint/__init__.py

@@ -0,0 +1,22 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+from typing import TYPE_CHECKING
+
+from modelscope.utils.import_utils import LazyImportModule
+
+if TYPE_CHECKING:
+    from .human_wholebody_keypoint_dataset import WholeBodyCocoTopDownDataset
+
+else:
+    _import_structure = {
+        'human_wholebody_keypoint_dataset': ['WholeBodyCocoTopDownDataset']
+    }
+
+    import sys
+
+    sys.modules[__name__] = LazyImportModule(
+        __name__,
+        globals()['__file__'],
+        _import_structure,
+        module_spec=__spec__,
+        extra_objects={},
+    )

modelscope/msdatasets/cv/human_wholebody_keypoint/human_wholebody_keypoint_dataset.py

@@ -0,0 +1,39 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+from easycv.datasets.pose import \
+    WholeBodyCocoTopDownDataset as _WholeBodyCocoTopDownDataset
+
+from modelscope.metainfo import Datasets
+from modelscope.msdatasets.cv.easycv_base import EasyCVBaseDataset
+from modelscope.msdatasets.task_datasets.builder import TASK_DATASETS
+from modelscope.utils.constant import Tasks
+
+
+@TASK_DATASETS.register_module(
+    group_key=Tasks.human_wholebody_keypoint,
+    module_name=Datasets.HumanWholeBodyKeypointDataset)
+class WholeBodyCocoTopDownDataset(EasyCVBaseDataset,
+                                  _WholeBodyCocoTopDownDataset):
+    """EasyCV dataset for human whole body 2d keypoints.
+
+    Args:
+        split_config (dict): Dataset root path from MSDataset, e.g.
+            {"train":"local cache path"} or {"evaluation":"local cache path"}.
+        preprocessor (Preprocessor): An optional preprocessor instance, please make sure the preprocessor fits for
+            the model if supplied. Not support yet.
+        mode: Training or Evaluation.
+    """
+
+    def __init__(self,
+                 split_config=None,
+                 preprocessor=None,
+                 mode=None,
+                 *args,
+                 **kwargs) -> None:
+        EasyCVBaseDataset.__init__(
+            self,
+            split_config=split_config,
+            preprocessor=preprocessor,
+            mode=mode,
+            args=args,
+            kwargs=kwargs)
+        _WholeBodyCocoTopDownDataset.__init__(self, *args, **kwargs)

modelscope/outputs.py

@@ -203,7 +203,7 @@ TASK_OUTPUTS = {
 
     # human body keypoints detection result for single sample
     # {
-    #   "poses": [
+    #   "keypoints": [
     #               [[x, y]*15],
     #               [[x, y]*15],
     #               [[x, y]*15]
@@ -220,7 +220,7 @@ TASK_OUTPUTS = {
     #             ]
     # }
     Tasks.body_2d_keypoints:
-    [OutputKeys.POSES, OutputKeys.SCORES, OutputKeys.BOXES],
+    [OutputKeys.KEYPOINTS, OutputKeys.SCORES, OutputKeys.BOXES],
 
     # 3D human body keypoints detection result for single sample
     # {
@@ -339,6 +339,21 @@ TASK_OUTPUTS = {
         OutputKeys.SCENE_META_LIST
     ],
 
+    # human whole body keypoints detection result for single sample
+    # {
+    #   "keypoints": [
+    #               [[x, y]*133],
+    #               [[x, y]*133],
+    #               [[x, y]*133]
+    #             ]
+    #   "boxes": [
+    #               [x1, y1, x2, y2],
+    #               [x1, y1, x2, y2],
+    #               [x1, y1, x2, y2],
+    #             ]
+    # }
+    Tasks.human_wholebody_keypoint: [OutputKeys.KEYPOINTS, OutputKeys.BOXES],
+
     # video summarization result for a single video
     # {
     #        "output":

modelscope/pipelines/builder.py

@@ -75,8 +75,6 @@ DEFAULT_MODEL_FOR_PIPELINE = {
      'damo/nlp_bart_text-error-correction_chinese'),
     Tasks.image_captioning: (Pipelines.image_captioning,
                              'damo/ofa_image-caption_coco_large_en'),
-    Tasks.image_body_reshaping: (Pipelines.image_body_reshaping,
-                                 'damo/cv_flow-based-body-reshaping_damo'),
     Tasks.image_portrait_stylization:
     (Pipelines.person_image_cartoon,
      'damo/cv_unet_person-image-cartoon_compound-models'),
@@ -159,6 +157,9 @@ DEFAULT_MODEL_FOR_PIPELINE = {
     Tasks.image_classification:
     (Pipelines.daily_image_classification,
      'damo/cv_vit-base_image-classification_Dailylife-labels'),
+    Tasks.image_object_detection:
+    (Pipelines.image_object_detection_auto,
+     'damo/cv_yolox_image-object-detection-auto'),
     Tasks.ocr_recognition:
     (Pipelines.ocr_recognition,
      'damo/cv_convnextTiny_ocr-recognition-general_damo'),
@@ -186,6 +187,9 @@ DEFAULT_MODEL_FOR_PIPELINE = {
                              'damo/cv_fft_inpainting_lama'),
     Tasks.video_inpainting: (Pipelines.video_inpainting,
                              'damo/cv_video-inpainting'),
+    Tasks.human_wholebody_keypoint:
+    (Pipelines.human_wholebody_keypoint,
+     'damo/cv_hrnetw48_human-wholebody-keypoint_image'),
     Tasks.hand_static: (Pipelines.hand_static,
                         'damo/cv_mobileface_hand-static'),
     Tasks.face_human_hand_detection:

modelscope/pipelines/cv/__init__.py

@@ -46,7 +46,10 @@ if TYPE_CHECKING:
     from .video_category_pipeline import VideoCategoryPipeline
     from .virtual_try_on_pipeline import VirtualTryonPipeline
     from .shop_segmentation_pipleline import ShopSegmentationPipeline
-    from .easycv_pipelines import EasyCVDetectionPipeline, EasyCVSegmentationPipeline, Face2DKeypointsPipeline
+    from .easycv_pipelines import (EasyCVDetectionPipeline,
+                                   EasyCVSegmentationPipeline,
+                                   Face2DKeypointsPipeline,
+                                   HumanWholebodyKeypointsPipeline)
     from .text_driven_segmentation_pipleline import TextDrivenSegmentationPipeline
     from .movie_scene_segmentation_pipeline import MovieSceneSegmentationPipeline
     from .mog_face_detection_pipeline import MogFaceDetectionPipeline
@@ -109,8 +112,10 @@ else:
         'virtual_try_on_pipeline': ['VirtualTryonPipeline'],
         'shop_segmentation_pipleline': ['ShopSegmentationPipeline'],
         'easycv_pipeline': [
-            'EasyCVDetectionPipeline', 'EasyCVSegmentationPipeline',
-            'Face2DKeypointsPipeline'
+            'EasyCVDetectionPipeline',
+            'EasyCVSegmentationPipeline',
+            'Face2DKeypointsPipeline',
+            'HumanWholebodyKeypointsPipeline',
         ],
         'text_driven_segmentation_pipeline':
         ['TextDrivenSegmentationPipeline'],

modelscope/pipelines/cv/body_2d_keypoints_pipeline.py

@@ -73,7 +73,7 @@ class Body2DKeypointsPipeline(Pipeline):
         if input[0] is None or input[1] is None:
             return {
                 OutputKeys.BOXES: [],
-                OutputKeys.POSES: [],
+                OutputKeys.KEYPOINTS: [],
                 OutputKeys.SCORES: []
             }
 
@@ -83,7 +83,7 @@ class Body2DKeypointsPipeline(Pipeline):
             result_boxes.append([box[0][0], box[0][1], box[1][0], box[1][1]])
         return {
             OutputKeys.BOXES: result_boxes,
-            OutputKeys.POSES: poses,
+            OutputKeys.KEYPOINTS: poses,
             OutputKeys.SCORES: scores
         }
 

modelscope/pipelines/cv/body_3d_keypoints_pipeline.py

@@ -145,7 +145,7 @@ class Body3DKeypointsPipeline(Pipeline):
             kps_2d = self.human_body_2d_kps_detector(frame)
             box = kps_2d['boxes'][
                 0]  # box: [[[x1, y1], [x2, y2]]], N human boxes per frame, [0] represent using first detected bbox
-            pose = kps_2d['poses'][0]  # keypoints: [15, 2]
+            pose = kps_2d['keypoints'][0]  # keypoints: [15, 2]
             score = kps_2d['scores'][0]  # keypoints: [15, 2]
             all_2d_poses.append(pose)
             all_boxes_with_socre.append(

modelscope/pipelines/cv/easycv_pipelines/__init__.py

@@ -7,11 +7,14 @@ if TYPE_CHECKING:
     from .detection_pipeline import EasyCVDetectionPipeline
     from .segmentation_pipeline import EasyCVSegmentationPipeline
     from .face_2d_keypoints_pipeline import Face2DKeypointsPipeline
+    from .human_wholebody_keypoint_pipeline import HumanWholebodyKeypointsPipeline
 else:
     _import_structure = {
         'detection_pipeline': ['EasyCVDetectionPipeline'],
         'segmentation_pipeline': ['EasyCVSegmentationPipeline'],
-        'face_2d_keypoints_pipeline': ['Face2DKeypointsPipeline']
+        'face_2d_keypoints_pipeline': ['Face2DKeypointsPipeline'],
+        'human_wholebody_keypoint_pipeline':
+        ['HumanWholebodyKeypointsPipeline'],
     }
 
     import sys

modelscope/pipelines/cv/easycv_pipelines/detection_pipeline.py

@@ -1,16 +1,28 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
+from typing import Any
+
 from modelscope.metainfo import Pipelines
+from modelscope.outputs import OutputKeys
 from modelscope.pipelines.builder import PIPELINES
-from modelscope.utils.constant import Tasks
+from modelscope.utils.constant import ModelFile, Tasks
+from modelscope.utils.cv.image_utils import \
+    show_image_object_detection_auto_result
 from .base import EasyCVPipeline
 
 
 @PIPELINES.register_module(
     Tasks.image_object_detection, module_name=Pipelines.easycv_detection)
+@PIPELINES.register_module(
+    Tasks.image_object_detection,
+    module_name=Pipelines.image_object_detection_auto)
 class EasyCVDetectionPipeline(EasyCVPipeline):
     """Pipeline for easycv detection task."""
 
-    def __init__(self, model: str, model_file_pattern='*.pt', *args, **kwargs):
+    def __init__(self,
+                 model: str,
+                 model_file_pattern=ModelFile.TORCH_MODEL_FILE,
+                 *args,
+                 **kwargs):
         """
             model (str): model id on modelscope hub or local model path.
             model_file_pattern (str): model file pattern.
@@ -21,3 +33,28 @@ class EasyCVDetectionPipeline(EasyCVPipeline):
             model_file_pattern=model_file_pattern,
             *args,
             **kwargs)
+
+    def show_result(self, img_path, result, save_path=None):
+        show_image_object_detection_auto_result(img_path, result, save_path)
+
+    def __call__(self, inputs) -> Any:
+        outputs = self.predict_op(inputs)
+
+        scores = []
+        labels = []
+        boxes = []
+        for output in outputs:
+            for score, label, box in zip(output['detection_scores'],
+                                         output['detection_classes'],
+                                         output['detection_boxes']):
+                scores.append(score)
+                labels.append(self.cfg.CLASSES[label])
+                boxes.append([b for b in box])
+
+        results = [{
+            OutputKeys.SCORES: scores,
+            OutputKeys.LABELS: labels,
+            OutputKeys.BOXES: boxes
+        } for output in outputs]
+
+        return results

modelscope/pipelines/cv/easycv_pipelines/human_wholebody_keypoint_pipeline.py

@@ -0,0 +1,65 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+import os.path
+from typing import Any
+
+from modelscope.metainfo import Pipelines
+from modelscope.outputs import OutputKeys
+from modelscope.pipelines.builder import PIPELINES
+from modelscope.utils.constant import ModelFile, Tasks
+from .base import EasyCVPipeline
+
+
+@PIPELINES.register_module(
+    Tasks.human_wholebody_keypoint,
+    module_name=Pipelines.human_wholebody_keypoint)
+class HumanWholebodyKeypointsPipeline(EasyCVPipeline):
+    """Pipeline for human wholebody 2d keypoints detection."""
+
+    def __init__(self,
+                 model: str,
+                 model_file_pattern=ModelFile.TORCH_MODEL_FILE,
+                 *args,
+                 **kwargs):
+        """
+            model (str): model id on modelscope hub or local model path.
+            model_file_pattern (str): model file pattern.
+        """
+        self.model_dir = model
+        super(HumanWholebodyKeypointsPipeline, self).__init__(
+            model=model,
+            model_file_pattern=model_file_pattern,
+            *args,
+            **kwargs)
+
+    def _build_predict_op(self, **kwargs):
+        """Build EasyCV predictor."""
+        from easycv.predictors.builder import build_predictor
+        detection_predictor_type = self.cfg['DETECTION']['type']
+        detection_model_path = os.path.join(
+            self.model_dir, self.cfg['DETECTION']['model_path'])
+        detection_cfg_file = os.path.join(self.model_dir,
+                                          self.cfg['DETECTION']['config_file'])
+        detection_score_threshold = self.cfg['DETECTION']['score_threshold']
+        self.cfg.pipeline.predictor_config[
+            'detection_predictor_config'] = dict(
+                type=detection_predictor_type,
+                model_path=detection_model_path,
+                config_file=detection_cfg_file,
+                score_threshold=detection_score_threshold)
+        easycv_config = self._to_easycv_config()
+        pipeline_op = build_predictor(self.cfg.pipeline.predictor_config, {
+            'model_path': self.model_path,
+            'config_file': easycv_config,
+            **kwargs
+        })
+        return pipeline_op
+
+    def __call__(self, inputs) -> Any:
+        outputs = self.predict_op(inputs)
+
+        results = [{
+            OutputKeys.KEYPOINTS: output['keypoints'],
+            OutputKeys.BOXES: output['boxes']
+        } for output in outputs]
+
+        return results

modelscope/utils/constant.py

@@ -29,6 +29,7 @@ class CVTasks(object):
     body_3d_keypoints = 'body-3d-keypoints'
     hand_2d_keypoints = 'hand-2d-keypoints'
     general_recognition = 'general-recognition'
+    human_wholebody_keypoint = 'human-wholebody-keypoint'
 
     image_classification = 'image-classification'
     image_multilabel_classification = 'image-multilabel-classification'

modelscope/utils/cv/image_utils.py

@@ -80,7 +80,7 @@ def realtime_object_detection_bbox_vis(image, bboxes):
 
 
 def draw_keypoints(output, original_image):
-    poses = np.array(output[OutputKeys.POSES])
+    poses = np.array(output[OutputKeys.KEYPOINTS])
     scores = np.array(output[OutputKeys.SCORES])
     boxes = np.array(output[OutputKeys.BOXES])
     assert len(poses) == len(scores) and len(poses) == len(boxes)
@@ -234,3 +234,35 @@ def show_video_summarization_result(video_in_path, result, video_save_path):
             video_writer.write(frame)
     video_writer.release()
     cap.release()
+
+
+def show_image_object_detection_auto_result(img_path,
+                                            detection_result,
+                                            save_path=None):
+    scores = detection_result[OutputKeys.SCORES]
+    labels = detection_result[OutputKeys.LABELS]
+    bboxes = detection_result[OutputKeys.BOXES]
+    img = cv2.imread(img_path)
+    assert img is not None, f"Can't read img: {img_path}"
+
+    for (score, label, box) in zip(scores, labels, bboxes):
+        cv2.rectangle(img, (int(box[0]), int(box[1])),
+                      (int(box[2]), int(box[3])), (0, 0, 255), 2)
+        cv2.putText(
+            img,
+            f'{score:.2f}', (int(box[0]), int(box[1])),
+            1,
+            1.0, (0, 255, 0),
+            thickness=1,
+            lineType=8)
+        cv2.putText(
+            img,
+            label, (int((box[0] + box[2]) * 0.5), int(box[1])),
+            1,
+            1.0, (0, 255, 0),
+            thickness=1,
+            lineType=8)
+
+    if save_path is not None:
+        cv2.imwrite(save_path, img)
+    return img

tests/pipelines/test_human_wholebody_keypoint.py

@@ -0,0 +1,40 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+import unittest
+
+import cv2
+
+from modelscope.outputs import OutputKeys
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+from modelscope.utils.test_utils import test_level
+
+
+class EasyCVFace2DKeypointsPipelineTest(unittest.TestCase):
+
+    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
+    def test_human_wholebody_keypoint(self):
+        img_path = 'data/test/images/keypoints_detect/img_test_wholebody.jpg'
+        model_id = 'damo/cv_hrnetw48_human-wholebody-keypoint_image'
+
+        human_wholebody_keypoint_pipeline = pipeline(
+            task=Tasks.human_wholebody_keypoint, model=model_id)
+        output = human_wholebody_keypoint_pipeline(img_path)[0]
+
+        output_keypoints = output[OutputKeys.KEYPOINTS]
+        output_pose = output[OutputKeys.BOXES]
+
+        human_wholebody_keypoint_pipeline.predict_op.show_result(
+            img_path,
+            output_keypoints,
+            output_pose,
+            scale=1,
+            save_path='human_wholebody_keypoint_ret.jpg')
+
+        for keypoint in output_keypoints:
+            self.assertEqual(keypoint.shape[0], 133)
+        for box in output_pose:
+            self.assertEqual(box.shape[0], 4)
+
+
+if __name__ == '__main__':
+    unittest.main()

tests/pipelines/test_object_detection.py

@@ -59,6 +59,18 @@ class ObjectDetectionTest(unittest.TestCase, DemoCompatibilityCheck):
     def test_demo_compatibility(self):
         self.compatibility_check()
 
+    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
+    def test_image_object_detection_auto_pipeline(self):
+        model_id = 'damo/cv_yolox_image-object-detection-auto'
+        test_image = 'data/test/images/auto_demo.jpg'
+
+        image_object_detection_auto = pipeline(
+            Tasks.image_object_detection, model=model_id)
+
+        result = image_object_detection_auto(test_image)[0]
+        image_object_detection_auto.show_result(test_image, result,
+                                                'auto_demo_ret.jpg')
+
 
 if __name__ == '__main__':
     unittest.main()