Update app.py

app.py

@@ -25,16 +25,10 @@ from mask_adapter.modeling.meta_arch.mask_adapter_head import build_mask_adapter
 from mask_adapter.data.datasets import openseg_classes
 
 COCO_CATEGORIES_pan = openseg_classes.get_coco_categories_with_prompt_eng()
-thing_classes = [k["name"] for k in COCO_CATEGORIES_pan if k["isthing"] == 1]
 stuff_classes = [k["name"] for k in COCO_CATEGORIES_pan]
 ADE20K_150_CATEGORIES_ = openseg_classes.get_ade20k_categories_with_prompt_eng()
-ade20k_thing_classes = [k["name"] for k in ADE20K_150_CATEGORIES_ if k["isthing"] == 1]
 ade20k_stuff_classes = [k["name"] for k in ADE20K_150_CATEGORIES_]
-class_names_coco_ade20k = thing_classes + stuff_classes + ade20k_thing_classes+ ade20k_stuff_classes
-
-
-
-
+class_names_coco_ade20k =  stuff_classes + ade20k_stuff_classes
 
 
 def setup_cfg(config_file):
@@ -125,7 +119,7 @@ def inference_point(input_img, img_state,class_names_input):
         class_names_input = class_names_coco_ade20k
         
     if class_names_input == class_names_coco_ade20k:
-        text_features = torch.from_numpy(np.load("./text_embedding/coco_ade20k_text_embedding.npy")).cuda()
+        text_features = torch.from_numpy(np.load("./text_embedding/coco_ade20k_text_embedding_new.npy")).cuda()
         _, visualized_output = demo.run_on_image_with_points(img_state.img, points,text_features)
     else:
         class_names_input = class_names_input.split(',')
@@ -167,7 +161,7 @@ def inference_box(input_img,  img_state,class_names_input):
         class_names_input = class_names_coco_ade20k
         
     if class_names_input == class_names_coco_ade20k:
-        text_features = torch.from_numpy(np.load("./text_embedding/coco_ade20k_text_embedding.npy")).cuda()
+        text_features = torch.from_numpy(np.load("./text_embedding/coco_ade20k_text_embedding_new.npy")).cuda()
         _, visualized_output = demo.run_on_image_with_boxes(img_state.img, bbox,text_features)
     else:
         class_names_input = class_names_input.split(',')