Create load_model.py

load_model.py

@@ -0,0 +1,16 @@
+import torch
+import os
+
+# Define the model architecture and other necessary functions/classes
+# ... (This would include the 'Pix2PixModelAdjusted', 'Opt', and any other necessary classes)
+
+# Initialize the model
+opt = Opt()
+model = Pix2PixModelAdjusted(opt)
+model.netG.eval()  # Set to evaluation mode
+
+# Load the trained weights
+model_path = "./latest_net_G.pth"  # Adjust the path if necessary
+model.netG.load_state_dict(torch.load(model_path))
+
+# The model is now initialized and ready for inferencing