Update

Signed-off-by: Jael Gu <mengjia.gu@zilliz.com>

isc.py

@@ -28,9 +28,9 @@ import sys
 
 import torch
 from torch import nn
-from torchvision import transforms
 from PIL import Image as PILImage
-import timm
+from timm.data import create_transform
+from timm.models import create_model, get_pretrained_cfg
 
 warnings.filterwarnings('ignore')
 log = logging.getLogger('isc_op')
@@ -41,7 +41,7 @@ _ = sys.modules[__name__]
 class Model:
     def __init__(self, timm_backbone, checkpoint_path, device):
         self.device = device
-        self.backbone = timm.create_model(timm_backbone, features_only=True, pretrained=False)
+        self.backbone = create_model(timm_backbone, features_only=True, pretrained=False)
         self.model = isc.create_model(pretrained=True, checkpoint_path=checkpoint_path, device=self.device,
                                       backbone=self.backbone, p=1.0, eval_p=1.0)
         self.model.eval()
@@ -79,12 +79,13 @@ class Isc(NNOperator):
 
         self.model = Model(self.timm_backbone, checkpoint_path, self.device)
 
-        self.tfms = transforms.Compose([
-            transforms.Resize((img_size, img_size)),
-            transforms.ToTensor(),
-            transforms.Normalize(mean=self.backbone.default_cfg['mean'],
-                                 std=self.backbone.default_cfg['std'])
-        ])
+        self.tfms = create_transform(
+                input_size=img_size,
+                interpolation=self.config['interpolation'],
+                mean=self.config['mean'],
+                std=self.config['std'],
+                crop_pct=self.config['crop_pct']
+            )
 
     def __call__(self, data: Union[List['towhee.types.Image'], 'towhee.types.Image']):
         if not isinstance(data, list):
@@ -113,9 +114,9 @@ class Isc(NNOperator):
         return self.model.model
 
     @property
-    def backbone(self):
-        backbone = timm.create_model(self.timm_backbone, features_only=True, pretrained=False)
-        return backbone
+    def config(self):
+        config = get_pretrained_cfg(self.timm_backbone)
+        return config
 
     def save_model(self, format: str = 'pytorch', path: str = 'default'):
         if path == 'default':