Update app.py

app.py

@@ -1,26 +1,20 @@
-from config import MLP
+from config import model
 from mmcv import Config
 from mmdet.models import build_detector
 import torch
 
 def main():
-    # Print model type from config
-    print(f"Model type: {MLP['type']}")
+    print(f"Model type: {model['type']}")
 
-    # Build the model from the config dict
-    model = build_detector(MLP, train_cfg=MLP.get('train_cfg'), test_cfg=MLP.get('test_cfg'))
+    detector = build_detector(model, train_cfg=model.get('train_cfg'), test_cfg=model.get('test_cfg'))
+    detector.eval()
 
-    # Set model to evaluation mode
-    model.eval()
+    print(detector)
 
-    # Print model architecture summary
-    print(model)
-
-    # Optional: dummy input test (batch of 1 image with 3 channels, 800x1333)
     dummy_input = torch.randn(1, 3, 800, 1333)
     with torch.no_grad():
-        result = model.forward_dummy(dummy_input)
-        print("Forward pass output:", result)
+        output = detector.forward_dummy(dummy_input)
+        print("Forward output:", output)
 
 if __name__ == "__main__":
     main()