Fix ClipSeg error

app.py

@@ -41,7 +41,7 @@ radio_options = [
     "Upload mask", 
     "Draw mask above",
     "Infer mask with MaskFormer", 
-    "Infer mask with CLIPSeg"
+    # "Infer mask with CLIPSeg"
 ]
 presets = {
     "custom": [None, None, None, None],
@@ -58,15 +58,15 @@ hparams_presets = {
 # ==========================
 #        CLIPSeg Init
 # ==========================
-clip_seg_model = CLIPDensePredT(version='ViT-B/16', reduce_dim=64)
-clip_seg_model.eval()
-clip_seg_model.load_state_dict(torch.load('clipseg/weights/rd64-uni.pth'), strict=False)
-clip_seg_model.to(device)
-clip_seg_transform = transforms.Compose([
-      transforms.ToTensor(),
-      transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
-      transforms.Resize((512, 512)),
-])
+# clip_seg_model = CLIPDensePredT(version='ViT-B/16', reduce_dim=64)
+# clip_seg_model.eval()
+# clip_seg_model.load_state_dict(torch.load('clipseg/weights/rd64-uni.pth'), strict=False)
+# clip_seg_model.to(device)
+# clip_seg_transform = transforms.Compose([
+#       transforms.ToTensor(),
+#       transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+#       transforms.Resize((512, 512)),
+# ])
 
 # ==========================
 #      MaskFormer Init
@@ -125,16 +125,16 @@ def activate_mask_upload(radio):
     is_upload = radio == radio_options[0]
     return gr.update(interactive=is_upload)
 
-def infer_clip_seg(img, source_prompt, threshold=100):
-    img = clip_seg_transform(img).unsqueeze(0)
-    with torch.no_grad():
-        preds = clip_seg_model(img, [source_prompt])[0]
-    mask_preds = torch.sigmoid(preds[0][0]).detach().cpu().numpy()
-    mask_preds = mask_preds * 255.0
-    mask_preds = np.where(mask_preds > threshold, 255, 0)
-    mask_preds = np.uint8(mask_preds)
-    mask_preds = Image.fromarray(mask_preds, "L")
-    return mask_preds
+# def infer_clip_seg(img, source_prompt, threshold=100):
+#     img = clip_seg_transform(img).unsqueeze(0)
+#     with torch.no_grad():
+#         preds = clip_seg_model(img, [source_prompt])[0]
+#     mask_preds = torch.sigmoid(preds[0][0]).detach().cpu().numpy()
+#     mask_preds = mask_preds * 255.0
+#     mask_preds = np.where(mask_preds > threshold, 255, 0)
+#     mask_preds = np.uint8(mask_preds)
+#     mask_preds = Image.fromarray(mask_preds, "L")
+#     return mask_preds
 
 def infer_maskformer(img, source_prompt):
     category_mapping = {cat["name"]: i for i, cat in enumerate(COCO_CATEGORIES)}
@@ -158,8 +158,8 @@ def get_mask(radio, image_upload, mask_upload, source_prompt, threshold=100):
         mask = image_upload["mask"]
     elif radio == radio_options[2]:
         mask = infer_maskformer(image_upload["image"], source_prompt)
-    elif radio == radio_options[3]:
-        mask = infer_clip_seg(image_upload["image"], source_prompt)
+    # elif radio == radio_options[3]:
+    #     mask = infer_clip_seg(image_upload["image"], source_prompt)
 
     if mask is None:
         raise gr.Error("Missing input mask. Try running Update Mask again.")