add save_model

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

timm_image.py

@@ -14,6 +14,8 @@
 
 import logging
 import numpy
+import os
+from pathlib import Path
 
 import towhee
 from towhee.operator.base import NNOperator, OperatorFlag
@@ -51,7 +53,8 @@ class TimmImage(NNOperator):
     def __init__(self, model_name: str, num_classes: int = 1000, skip_preprocess: bool = False) -> None:
         super().__init__()
         self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
-        self.model = create_model(model_name, pretrained=True, num_classes=num_classes)
+        self.model_name = model_name
+        self.model = create_model(self.model_name, pretrained=True, num_classes=num_classes)
         self.model.to(self.device)
         self.model.eval()
         config = resolve_data_config({}, model=self.model)
@@ -73,6 +76,18 @@ class TimmImage(NNOperator):
         vec = features.flatten().detach().numpy()
         return vec
 
+    def save_model(self, jit: bool = True, destination: str = 'default'):
+        if destination == 'default':
+            path = str(Path(__file__).parent)
+            destination = os.path.join(path, self.model_name + '.pt')
+        if jit:
+            try:
+                traced_model = torch.jit.script(self.model)
+                torch.jit.save(traced_model, destination)
+            except Exception as e:
+                raise RuntimeError(f'Fail to save as torchscript: {e}.')
+        else:
+            torch.save(self.model, destination)
 
 # if __name__ == '__main__':
 #     from towhee import ops
@@ -85,3 +100,6 @@ class TimmImage(NNOperator):
 #     op = TimmImage('resnet50')
 #     out = op(img)
 #     print(out)
+#    op.model = torch.jit.load('resnet50.pt')
+#    out2 = op(img)
+#    print(out2)