[to #42322933]修复pipeline串联时collate_fn异常

修复pipeline串联时collate_fn异常
        Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10457058

modelscope/pipelines/base.py

@@ -433,6 +433,8 @@ def collate_fn(data, device):
     if isinstance(data, dict) or isinstance(data, Mapping):
         return type(data)({k: collate_fn(v, device) for k, v in data.items()})
     elif isinstance(data, (tuple, list)):
+        if 0 == len(data):
+            return torch.Tensor([])
         if isinstance(data[0], (int, float)):
             return default_collate(data).to(device)
         else:

modelscope/pipelines/cv/body_3d_keypoints_pipeline.py

@@ -143,6 +143,13 @@ class Body3DKeypointsPipeline(Pipeline):
         max_frame = self.keypoint_model_3d.cfg.model.INPUT.MAX_FRAME  # max video frame number to be predicted 3D joints
         for i, frame in enumerate(video_frames):
             kps_2d = self.human_body_2d_kps_detector(frame)
+            if [] == kps_2d.get('boxes'):
+                res = {
+                    'success': False,
+                    'msg': f'fail to detect person at image frame {i}'
+                }
+                return res
+
             box = kps_2d['boxes'][
                 0]  # box: [[[x1, y1], [x2, y2]]], N human boxes per frame, [0] represent using first detected bbox
             pose = kps_2d['keypoints'][0]  # keypoints: [15, 2]
@@ -180,7 +187,15 @@ class Body3DKeypointsPipeline(Pipeline):
         return res
 
     def postprocess(self, input: Dict[str, Any], **kwargs) -> Dict[str, Any]:
-        res = {OutputKeys.KEYPOINTS: [], OutputKeys.TIMESTAMPS: []}
+        output_video_path = kwargs.get('output_video', None)
+        if output_video_path is None:
+            output_video_path = tempfile.NamedTemporaryFile(suffix='.mp4').name
+
+        res = {
+            OutputKeys.KEYPOINTS: [],
+            OutputKeys.TIMESTAMPS: [],
+            OutputKeys.OUTPUT_VIDEO: output_video_path
+        }
 
         if not input['success']:
             pass
@@ -189,10 +204,6 @@ class Body3DKeypointsPipeline(Pipeline):
             pred_3d_pose = poses.data.cpu().numpy()[
                 0]  # [frame_num, joint_num, joint_dim]
 
-            output_video_path = kwargs.get('output_video', None)
-            if output_video_path is None:
-                output_video_path = tempfile.NamedTemporaryFile(
-                    suffix='.mp4').name
             if 'render' in self.keypoint_model_3d.cfg.keys():
                 self.render_prediction(pred_3d_pose, output_video_path)
                 res[OutputKeys.OUTPUT_VIDEO] = output_video_path