Update gradio_utils/utils.py

gradio_utils/utils.py

@@ -114,15 +114,6 @@ def estimate(model, data):
     with torch.no_grad():
         return model(data)
 
-
-# Custom JSON encoder to handle non-serializable objects
-class CustomEncoder(json.JSONEncoder):
-    def default(self, obj):
-        if isinstance(obj, np.ndarray):
-            return obj.tolist()
-        return super().default(obj)
-        
-
 def process(query_img, state,
             cfg_path='configs/test/1shot_split1.py',
             checkpoint_path='ckpt/1shot_split1.pth'):
@@ -200,22 +191,7 @@ def process(query_img, state,
     if fp16_cfg is not None:
         wrap_fp16_model(model)
     load_checkpoint(model, checkpoint_path, map_location='cpu')
-	
-    data["img_s"] = data["img_s"][0].cpu().numpy().tolist()
-    data["img_q"] = data["img_q"].cpu().numpy().tolist()
-    data['target_weight_s'][0] = data['target_weight_s'][0].cpu().numpy().tolist()
-    data['target_s'][0] = data['target_s'][0].cpu().numpy().tolist()
-    
-    data['img_metas'][0]['sample_joints_3d'][0] = data['img_metas'][0]['sample_joints_3d'][0].cpu().tolist()
-    data['img_metas'][0]['query_joints_3d'] = data['img_metas'][0]['query_joints_3d'].cpu().tolist()
-    data['img_metas'][0]['sample_center'][0] = data['img_metas'][0]['sample_center'][0].cpu().tolist()
-    data['img_metas'][0]['query_center'] = data['img_metas'][0]['query_center'].cpu().tolist()
-    data['img_metas'][0]['sample_scale'][0] = data['img_metas'][0]['sample_scale'][0].cpu().tolist()
-    data['img_metas'][0]['query_scale'] = data['img_metas'][0]['query_scale'].cpu().tolist()
-    
     model.eval()
-    
-    str_data = json.dumps(data, cls=CustomEncoder)
     outputs = estimate(model, str_data)
 	
     # visualize results