Comments (2)
Thanks for your wonderful work. When I run the test for the 2D Uncertainty_Rectified_Pyramid_Consistency according to your advice. I met the problem 'Traceback (most recent call last): File "/home/b417/xb/5_SSL4MIS-master/SSL4MIS-master/code/test_2D_fully.py", line 121, in metric = Inference(FLAGS) File "/home/b417/xb/5_SSL4MIS-master/SSL4MIS-master/code/test_2D_fully.py", line 110, in Inference case, net, test_save_path, FLAGS) File "/home/b417/xb/5_SSL4MIS-master/SSL4MIS-master/code/test_2D_fully.py", line 64, in test_single_volume out_main, dim=1), dim=1).squeeze(0) TypeError: softmax() received an invalid combination of arguments - got (tuple, dim=int), but expected one of:
* (Tensor input, name dim, *, torch.dtype dtype) * (Tensor input, int dim, torch.dtype dtype)'. I do not know how to deal with it. Could you please tell me how to solve it? Thanks in advance ![1](https://user-images.githubusercontent.com/25636252/149947463-5b1ac622-70d4-4084-a28e-1b9b610e6c0e.PNG) ![2](https://user-images.githubusercontent.com/25636252/149947489-80cd3496-d264-4dc4-9bc2-3c6e9093ea30.PNG) .
这个是在 test_2D_fully.py
中 将 if FLAGS.model == "unet_urds"
: 改成 if FLAGS.model == "unet_urpc":
这个要根据里面来改
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Hi,
I am so sorry to reply to you, not in time, as I am working on a weakly-supervised segmentation project. For this issue, the URPC produces several predictions, you just need to use the full resolution prediction, and ignore others, like this line.
Best,
Xiangde.
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