Update detectron2/modeling/meta_arch/clip_rcnn.py

detectron2/modeling/meta_arch/clip_rcnn.py

@@ -378,7 +378,7 @@ class CLIPRCNN(nn.Module):
                 for p_i, pad_image in enumerate(images):
                     to_save = pad_image.permute(1, 2, 0).numpy()
                     to_save = Image.fromarray(np.array(to_save, np.uint8))
-                    to_save.save("output/regions/" + f_n.split("/")[-1].split(".")[0] + "-{}.png".format(p_i))
+                    #to_save.save("output/regions/" + f_n.split("/")[-1].split(".")[0] + "-{}.png".format(p_i))
                     pass
 
         # crop image region
@@ -1492,7 +1492,7 @@ def visualize_proposals(batched_inputs, proposals, input_format, vis_pretrain=Fa
             prop_img = v_pred.get_image()
             vis_img = prop_img
             to_save = Image.fromarray(np.array(vis_img, np.uint8))
-            to_save.save("output/regions/" + str(i) + ".png")
+            #to_save.save("output/regions/" + str(i) + ".png")
             #break  # only visualize one image in a batch
     else:
         for input, prop in zip(batched_inputs, proposals):
@@ -1507,7 +1507,7 @@ def visualize_proposals(batched_inputs, proposals, input_format, vis_pretrain=Fa
             vis_img = prop_img
             # f_n = input['file_name']
             to_save = Image.fromarray(np.array(vis_img, np.uint8))
-            to_save.save("output/regions/" + "proposals.png")
+            #to_save.save("output/regions/" + "proposals.png")
             #break  # only visualize one image in a batch
 
 def visualize_results(batched_inputs, results, input_format, vis_pretrain=False):
@@ -1537,7 +1537,7 @@ def visualize_results(batched_inputs, results, input_format, vis_pretrain=False)
             prop_img = v_pred.get_image()
             vis_img = prop_img
             to_save = Image.fromarray(np.array(vis_img, np.uint8))
-            # to_save.save("output/regions/" + str(i) + ".png")
+            #to_save.save("output/regions/" + str(i) + ".png")
             #break  # only visualize one image in a batch
     else:
         for input, prop in zip(batched_inputs, results):
@@ -1552,6 +1552,6 @@ def visualize_results(batched_inputs, results, input_format, vis_pretrain=False)
             vis_img = prop_img
             # f_n = input['file_name']
             to_save = Image.fromarray(np.array(vis_img, np.uint8))
-            to_save.save("output/regions/" + "results.png")
+            #to_save.save("output/regions/" + "results.png")
             #break  # only visualize one image in a batch
     return to_save