Update example.py

example.py

@@ -3,11 +3,11 @@ import torch
 from model import SRResNet  # Import your model class
 
 # Load the pre-trained model
-model = MeteoGAN(in_channels=3, out_channels=3, upscale=4)
+model = SRResNet(in_channels=12, out_channels=72, upscale=1)
 model.load_state_dict(torch.load("best_netG.pth"))  # Load model weights
 
 # Create a random input tensor (e.g., for testing purposes)
-input_tensor = torch.rand(1, 3, 128, 128)  # Batch size 1, 3 channels, 128x128 image
+input_tensor = torch.rand(1, 12, 128, 128)  # Batch size 1, 3 channels, 128x128 image
 
 # Perform inference
 output_tensor = model(input_tensor)