add movinet

Signed-off-by: gexy5 <xinyu.ge@zilliz.com>

movinet.py

@@ -41,14 +41,14 @@ class Movinet(NNOperator):
     def __init__(self,
                  model_name: str = 'movineta0',
                  framework: str = 'pytorch',
-                 input_type: str = 'video',
+                 casual: str = False,
                  skip_preprocess: bool = False,
                  classmap: dict = None,
                  topk: int = 5,
                  ):
         super().__init__(framework=framework)
         self.model_name = model_name
-        self.input_type = input_type
+        self.casual = casual
         self.skip_preprocess = skip_preprocess
         self.topk = topk
         self.dataset_name = 'kinetics_600'
@@ -65,7 +65,7 @@ class Movinet(NNOperator):
         else:
             self.classmap = classmap
         self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
-        self.model = create_model(model_name=model_name, pretrained=True, device=self.device)
+        self.model = create_model(model_name=model_name, pretrained=True, casual=self.casual, device=self.device)
         self.input_mean=[0.485, 0.456, 0.406]
         self.input_std=[0.229, 0.224, 0.225]
         self.transform_cfgs = get_configs(
@@ -107,7 +107,7 @@ class Movinet(NNOperator):
         feats = self.model.forward_features(inputs)
         features = feats.to('cpu').squeeze(0).detach().numpy()
 
-        outs = self.model.head(feats, input_type = self.input_type)
+        outs = self.model.head(feats)
         post_act = torch.nn.Softmax(dim=1)
         preds = post_act(outs)
         pred_scores, pred_classes = preds.topk(k=self.topk)