Pytorch TypeError when running examples

[ravenslav]

Hi,
I am trying to run your example files.
I first ran train_influence_functions.py, which ran fine, and then test_influence_functions.py, where I get the following output and error:

Files already downloaded and verified
Files already downloaded and verified
2022-06-24 10:58:49,474: Running on: 1 images per class.
2022-06-24 10:58:49,475: Starting at img number: 0 per class.

/usr/local/lib/python3.7/dist-packages/pytorch_influence_functions/influence_function.py:70: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.
  y = torch.nn.functional.log_softmax(y)

Calc. s_test recursions: [=================================================] 1 / 1
Averaging r-times: [=======================================================] 1 / 1
Calc. influence function: [========================================] 49999 / 50000
Calc. influence function: [========================================] 50000 / 50000

---------------------------------------------------------------------------

TypeError                                 Traceback (most recent call last)

[<ipython-input-17-f738cfd2c21e>](https://localhost:8080/#) in <module>()
      3 trainloader, testloader = load_data()
      4 ptif.init_logging()
----> 5 inf = ptif.calc_img_wise(config, model, trainloader, testloader)

5 frames

[/usr/local/lib/python3.7/dist-packages/pytorch_influence_functions/calc_influence_function.py](https://localhost:8080/#) in calc_img_wise(config, model, train_loader, test_loader)
    469         influence, harmful, helpful, _ = calc_influence_single(
    470             model, train_loader, test_loader, test_id_num=i, gpu=config['gpu'],
--> 471             recursion_depth=config['recursion_depth'], r=config['r_averaging'])
    472         end_time = time.time()
    473 

[/usr/local/lib/python3.7/dist-packages/pytorch_influence_functions/calc_influence_function.py](https://localhost:8080/#) in calc_influence_single(model, train_loader, test_loader, test_id_num, gpu, recursion_depth, r, s_test_vec, time_logging)
    344         display_progress("Calc. influence function: ", i, train_dataset_size)
    345 
--> 346     harmful = np.argsort(influences)
    347     helpful = harmful[::-1]
    348 

<__array_function__ internals> in argsort(*args, **kwargs)

[/usr/local/lib/python3.7/dist-packages/numpy/core/fromnumeric.py](https://localhost:8080/#) in argsort(a, axis, kind, order)
   1112 
   1113     """
-> 1114     return _wrapfunc(a, 'argsort', axis=axis, kind=kind, order=order)
   1115 
   1116 

[/usr/local/lib/python3.7/dist-packages/numpy/core/fromnumeric.py](https://localhost:8080/#) in _wrapfunc(obj, method, *args, **kwds)
     52     bound = getattr(obj, method, None)
     53     if bound is None:
---> 54         return _wrapit(obj, method, *args, **kwds)
     55 
     56     try:

[/usr/local/lib/python3.7/dist-packages/numpy/core/fromnumeric.py](https://localhost:8080/#) in _wrapit(obj, method, *args, **kwds)
     41     except AttributeError:
     42         wrap = None
---> 43     result = getattr(asarray(obj), method)(*args, **kwds)
     44     if wrap:
     45         if not isinstance(result, mu.ndarray):

[/usr/local/lib/python3.7/dist-packages/torch/_tensor.py](https://localhost:8080/#) in __array__(self, dtype)
    731         if dtype is None:
    732             return self.numpy()
--> 733         else:
    734             return self.numpy().astype(dtype, copy=False)
    735 

TypeError: can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.

I can't figure out why it is not working, as I assume it worked for you and I haven't changed anything.
Does anyone else have this issue or can tell me how to solve it?

[janellelines]

I also am having this issue and am wondering how to solve it.

[alycia-noel]

In this file '/usr/local/lib/python3.7/dist-packages/pytorch_influence_functions/calc_influence_function.py' change line 346 to be harmful = np.argsort(influences.cpu()).

[1yanzeyu]

insert “influences = [i.cpu() for i in influences]” before the wrong sentence