add3

Signed-off-by: xujinling <jinling.xu@zilliz.com>

video_swin_transformer.py

@@ -66,6 +66,26 @@ class VideoSwinTransformer(NNOperator):
                                             patch_norm=self.model_configs['patch_norm'],
                                             device=self.device)
 
+        self.transform_cfgs = get_configs(
+            side_size=224,
+            crop_size=224,
+            num_frames=4,
+            mean=[0.48145466, 0.4578275, 0.40821073],
+            std=[0.26862954, 0.26130258, 0.27577711],
+        )
+
+    def decoder_video(self,  data: List[VideoFrame]):
+        video = numpy.stack([img.astype(numpy.float32) / 255. for img in data], axis=0)
+        assert len(video.shape) == 4
+        video = video.transpose(3, 0, 1, 2)  # twhc -> ctwh
+        video = transform_video(
+            video=video,
+            **self.transform_cfgs
+        )
+        # [B x C x T x H x W]
+        video = video.to(self.device)[None, ...]
+        return video
+
     def __call__(self, video: List[VideoFrame]):
         """
         Args:
@@ -78,21 +98,8 @@ class VideoSwinTransformer(NNOperator):
             OR emb
                 Video embedding.
         """
-        # Convert list of towhee.types.Image to numpy.ndarray in float32
-        video = numpy.stack([img.astype(numpy.float32)/255. for img in video], axis=0)
-        assert len(video.shape) == 4
-        video = video.transpose(3, 0, 1, 2)  # twhc -> ctwh
-
-        # Transform video data given configs
-        if self.skip_preprocess:
-            self.cfg.update(num_frames=None)
-
-        data = transform_video(
-            video=video,
-            **self.cfg
-        )
-        inputs = data.to(self.device)[None, ...]
-
+        inputs = self.decoder_video(video)
+        # inputs [B x C x T x H x W]
         feats = self.model.forward_features(inputs)
         features = feats.to('cpu').squeeze(0).detach().numpy()