init

app.py

@@ -105,15 +105,19 @@ def create_refseg_demo(model, tokenizer, device):
                            
 
 def main():
+    upload_2_models = True
+
     opt = TestOptions().initialize()
     args = opt.parse_args()
     
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    #model = EVPDepth(args=args, caption_aggregation=True)
-    #model.to(device)
-    #model_weight = torch.load('best_model_nyu.ckpt', map_location=device)['model']
-    #model.load_state_dict(model_weight, strict=False)
-    #model.eval()
+    if upload_2_models:
+        model = EVPDepth(args=args, caption_aggregation=True)
+        model.to(device)
+        model_weight = torch.load('best_model_nyu.ckpt', map_location=device)['model']
+        model.load_state_dict(model_weight, strict=False)
+        model.eval()
+    
     tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
     model_refseg = EVPRefer()
     model_refseg.to(device)
@@ -133,8 +137,9 @@ def main():
     with gr.Blocks() as demo:
         gr.Markdown(title)
         gr.Markdown(description)
-        #with gr.Tab("Depth Prediction"):
-        #    create_depth_demo(model_refseg, device)
+        if upload_2_models:
+            with gr.Tab("Depth Prediction"):
+                create_depth_demo(model, device)
         with gr.Tab("Referring Segmentation"):
             create_refseg_demo(model_refseg, tokenizer, device)
         gr.HTML('''<br><br><br><center>You can duplicate this Space to skip the queue:<a href="https://huggingface.co/spaces/MykolaL/evp?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a><br>