Update mono/utils/do_test.py

mono/utils/do_test.py

@@ -210,10 +210,12 @@ def transform_test_data_scalecano(rgb, intrinsic, data_basic):
 
     rgb = torch.from_numpy(rgb.transpose((2, 0, 1))).float()
     rgb = torch.div((rgb - mean), std)
-    rgb = rgb[None, :, :, :].cuda()
+    #rgb = rgb[None, :, :, :].cuda()
+    rgb = rgb[None, :, :, :]
     
     cam_model = torch.from_numpy(cam_model.transpose((2, 0, 1))).float()
-    cam_model = cam_model[None, :, :, :].cuda()
+    #cam_model = cam_model[None, :, :, :].cuda()
+    cam_model = cam_model[None, :, :, :]
     cam_model_stacks = [
         torch.nn.functional.interpolate(cam_model, size=(cam_model.shape[2]//i, cam_model.shape[3]//i), mode='bilinear', align_corners=False)
         for i in [2, 4, 8, 16, 32]
@@ -276,7 +278,8 @@ def do_scalecano_test_with_custom_data(
 
             pred_depth = torch.nn.functional.interpolate(pred_depth[None, None, :, :], (gt_depth.shape[0], gt_depth.shape[1]), mode='bilinear').squeeze() # to original size
 
-            gt_depth = torch.from_numpy(gt_depth).cuda()
+            #gt_depth = torch.from_numpy(gt_depth).cuda()
+            gt_depth = torch.from_numpy(gt_depth)
 
             pred_depth_median = pred_depth * gt_depth[gt_depth != 0].median() / pred_depth[gt_depth != 0].median()
             pred_global, _ = align_scale_shift(pred_depth, gt_depth)