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app.py

@@ -75,8 +75,8 @@ demo = gr.Interface(
     inputs=[
         gr.Video(label="Upload a Video （e.g. mp4）"),
         gr.Checkbox(label="Use Finetuning?", value=False),
-        gr.Dropdown(label="Train Dataset Name", choices=["konvid_1k", "youtube_ugc", "live_vqc", "lsvq_train", "other"], value="lsvq_train"),
-        gr.Dropdown(label="Test Dataset Name", choices=["konvid_1k", "youtube_ugc", "live_vqc", "lsvq", "other"], value="konvid_1k"),
+        gr.Dropdown(label="Train Dataset Name", choices=["konvid_1k", "youtube_ugc", "live_vqc", "lsvq_train"], value="lsvq_train"),
+        gr.Dropdown(label="Test Dataset Name", choices=["konvid_1k", "youtube_ugc", "live_vqc", "lsvq"], value="konvid_1k"),
         gr.Dropdown(label="Our Models", choices=["diva-vqa", "diva-vqa_large"], value="diva-vqa_large")
     ],
     outputs=gr.Textbox(label="Predicted Perceptual Quality Score (0–100)"),

demo_test.py

@@ -51,7 +51,7 @@ def load_model(config, device, input_features=11520):
         else:
             model_path = os.path.join(config.save_path, f"wo_finetune/{config.train_data_name}_{config.network_name}_{config.model_name}_{config.select_criteria}"
                                                        f"_trained_median_model_param.pth")
-    # print("Loading model from:", model_path)
+    print("Loading model from:", model_path)
     state_dict = torch.load(model_path, map_location=device)
     fixed_state_dict = fix_state_dict(state_dict)
     try: