upload

run.py

@@ -34,18 +34,18 @@ def pred(src):
     my_sam_model.load_state_dict(torch.load("sam_model.pth", map_location=torch.device('cpu')))
 
     new_image = np.array(Image.open(src))
-    inputs = processor(new_image, return_tensors="pt")
-    my_sam_model.eval()
-
-    # forward pass
-    with torch.no_grad():
-        outputs = my_sam_model(**inputs, multimask_output=False)
-
-    # apply sigmoid
-    single_patch_prob = torch.sigmoid(outputs.pred_masks.squeeze(1))
-    # convert soft mask to hard mask
-    single_patch_prob = single_patch_prob.cpu().numpy().squeeze()
-    single_patch_prediction = (single_patch_prob > 0.5).astype(np.uint8)
+    # inputs = processor(new_image, return_tensors="pt")
+    # my_sam_model.eval()
+
+    # # forward pass
+    # with torch.no_grad():
+    #     outputs = my_sam_model(**inputs, multimask_output=False)
+
+    # # apply sigmoid
+    # single_patch_prob = torch.sigmoid(outputs.pred_masks.squeeze(1))
+    # # convert soft mask to hard mask
+    # single_patch_prob = single_patch_prob.cpu().numpy().squeeze()
+    # single_patch_prediction = (single_patch_prob > 0.5).astype(np.uint8)
     # patches = customized_patchify(new_image)
 
     # # Define the size of your array