feat : update fpn class file

models/fpn_inception.py

@@ -3,6 +3,9 @@ import torch.nn as nn
 from torchsummary import summary
 from pretrainedmodels import inceptionresnetv2
 import torch.nn.functional as F
+import os
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 class FPNHead(nn.Module):
     def __init__(self, num_in, num_mid, num_out):
@@ -91,7 +94,16 @@ class FPN(nn.Module):
         """
 
         super().__init__()
-        self.inception = inceptionresnetv2(num_classes=1000, pretrained='imagenet')
+        #self.inception = inceptionresnetv2(num_classes=1000, pretrained='imagenet')
+        self.inception = inceptionresnetv2(num_classes=1000, pretrained=None)
+        # 2️⃣ 載入本地權重
+        weight_path = os.path.join("model", "inceptionresnetv2_imagenet.pth")
+        if os.path.exists(weight_path):
+            state_dict = torch.load(weight_path, map_location=device)
+            self.inception.load_state_dict(state_dict)
+            print("Loaded local inceptionresnetv2_imagenet.pth successfully!")
+        else:
+            print(f"Warning: {weight_path} not found. Using randomly initialized weights.")
 
         self.enc0 = self.inception.conv2d_1a
         self.enc1 = nn.Sequential(