substitute face detection model in skin_retouching_pipeline.py

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10909902

modelscope/pipelines/cv/skin_retouching_pipeline.py

@@ -15,11 +15,10 @@ from modelscope.models.cv.skin_retouching.detection_model.detection_unet_in impo
     DetectionUNet
 from modelscope.models.cv.skin_retouching.inpainting_model.inpainting_unet import \
     RetouchingNet
-from modelscope.models.cv.skin_retouching.retinaface.predict_single import \
-    Model
 from modelscope.models.cv.skin_retouching.unet_deploy import UNet
 from modelscope.models.cv.skin_retouching.utils import *  # noqa F403
 from modelscope.outputs import OutputKeys
+from modelscope.pipelines import pipeline
 from modelscope.pipelines.base import Input, Pipeline
 from modelscope.pipelines.builder import PIPELINES
 from modelscope.preprocessors import LoadImage
@@ -48,8 +47,6 @@ class SkinRetouchingPipeline(Pipeline):
 
         device = create_device(self.device_name)
         model_path = os.path.join(self.model, ModelFile.TORCH_MODEL_FILE)
-        detector_model_path = os.path.join(
-            self.model, 'retinaface_resnet50_2020-07-20_old_torch.pth')
         local_model_path = os.path.join(self.model, 'joint_20210926.pth')
         skin_model_path = os.path.join(self.model, ModelFile.TF_GRAPH_FILE)
 
@@ -58,10 +55,9 @@ class SkinRetouchingPipeline(Pipeline):
             torch.load(model_path, map_location='cpu')['generator'])
         self.generator.eval()
 
-        self.detector = Model(max_size=512, device=device)
-        state_dict = torch.load(detector_model_path, map_location='cpu')
-        self.detector.load_state_dict(state_dict)
-        self.detector.eval()
+        det_model_id = 'damo/cv_resnet50_face-detection_retinaface'
+        self.detector = pipeline(Tasks.face_detection, model=det_model_id)
+        self.detector.detector.to(device)
 
         self.local_model_path = local_model_path
         ckpt_dict_load = torch.load(self.local_model_path, map_location='cpu')
@@ -136,9 +132,18 @@ class SkinRetouchingPipeline(Pipeline):
                     (rgb_image.shape[0], rgb_image.shape[1], 3),
                     dtype=np.float32) * 0.5
 
-            results = self.detector.predict_jsons(
-                rgb_image
-            )  # list, [{'bbox':, [x1, y1, x2, y2], 'score'...}, ...]
+            det_results = self.detector(rgb_image)
+            # list, [{'bbox':, [x1, y1, x2, y2], 'score'...}, ...]
+            results = []
+            for i in range(len(det_results['scores'])):
+                info_dict = {}
+                info_dict['bbox'] = np.array(det_results['boxes'][i]).astype(
+                    np.int32).tolist()
+                info_dict['score'] = det_results['scores'][i]
+                info_dict['landmarks'] = np.array(
+                    det_results['keypoints'][i]).astype(np.int32).reshape(
+                        5, 2).tolist()
+                results.append(info_dict)
 
             crop_bboxes = get_crop_bbox(results)