@AishaAlaagib The underlying root cause here is that this code was written using a very old version of PyTorch (I think maybe 0.2?), and there have been a bunch of breaking changes to PyTorch since then which means that the code makes many assumptions about the shapes and types of tensors which will not hold in more recent PyTorch releases.

For this error in particular, the module network implements forward passes for a couple different types of program representations, and tries to route to the correct implementation based on the type and shape of the program object: https://github.com/facebookresearch/clevr-iep/blob/master/iep/models/module_net.py#L233.

In this case, it looks like this line got executed: https://github.com/facebookresearch/clevr-iep/blob/master/iep/models/module_net.py#L234 (which expects the program to be in some JSON format); however based on your output from print(f) I think your program is actually a 2D tensor, so this line should have executed: https://github.com/facebookresearch/clevr-iep/blob/master/iep/models/module_net.py#L236 (which assumes the program is stored as a tensor).

You should debug around here to see why the wrong branch of this condition executed.

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I'm getting the same error.. Were you able to fix it ?

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I'm getting the same error.. Were you able to fix it ?

sorry. I don't fix it. If u solve it, plz help me to do this.

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okay, I will try.

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change the seq2seq.py > function reinforce_sample > forloop into

for t in range(T):
# logprobs is N x 1 x V
logprobs, h, c = self.decoder(encoded, cur_input, h0=h, c0=c)
logprobs = logprobs / temperature
probs = F.softmax(logprobs.view(N, -1),dim=None) # Now N x V
if argmax:
_, cur_output = probs.max(1)
cur_output = cur_output.unsqueeze(0)
else:
cur_output = probs.multinomial() # Now N x 1
self.multinomial_outputs.append(cur_output)
self.multinomial_probs.append(probs)
cur_output_data = cur_output.data.cpu()
#not_done = logical_not(done)
not_done= np.where(done.data.cpu().numpy() == 0)
#y[:, t][not_done] = cur_output_data[not_done]
y[not_done, t] = cur_output_data[not_done]
done = logical_or(done, cur_output_data.cpu() == self.END)
cur_input = cur_output
if done.sum() == N:
break

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Hi
i got same issue
have u solve it ？
if you did plz let me know
Thanks!

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@PuPuLL I have solved it. My above comment is the code. The problem is that the different supports of indexing a tensor array in Pytorch 0.1 and Pytorch 0.4.

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@PuPuLL I have solved it. My above comment is the code. The problem is that the different supports of indexing a tensor array in Pytorch 0.1 and Pytorch 0.4.

OK，I will try it. Thx!

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change the seq2seq.py > function reinforce_sample > forloop into

for t in range(T):

logprobs is N x 1 x V

logprobs, h, c = self.decoder(encoded, cur_input, h0=h, c0=c)
logprobs = logprobs / temperature
probs = F.softmax(logprobs.view(N, -1),dim=None) # Now N x V
if argmax:
_, cur_output = probs.max(1)
cur_output = cur_output.unsqueeze(0)
else:
cur_output = probs.multinomial() # Now N x 1
self.multinomial_outputs.append(cur_output)
self.multinomial_probs.append(probs)
cur_output_data = cur_output.data.cpu()
#not_done = logical_not(done)
not_done= np.where(done.data.cpu().numpy() == 0)
#y[:, t][not_done] = cur_output_data[not_done]
y[not_done, t] = cur_output_data[not_done]
done = logical_or(done, cur_output_data.cpu() == self.END)
cur_input = cur_output
if done.sum() == N:
break

sorry，I just got the message.
Thx!

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@PuPuLL I have solved it. My above comment is the code. The problem is that the different supports of indexing a tensor array in Pytorch 0.1 and Pytorch 0.4.

that works! thank you

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Hi,

I am using python 3.4 and pytorch 1.1.0
I got the same error like yours and I fix it after I got error

Traceback (most recent call last):
File "clevr-iep/iep/train_model.py", line 490, in
main(args)
File "clevr-iep/iep/train_model.py", line 151, in main
train_loop(args, train_loader, val_loader)
File "clevr-iep/iep/train_model.py", line 240, in train_loop
scores = execution_engine(feats_var, programs_pred)
File "/home/aalaagib/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py", line 493, in call
result = self.forward(*input, **kwargs)
File "/home/aalaagib/data/clevr-iep/iep/models/module_net.py", line 239, in forward
final_module_outputs = self._forward_modules_json(feats, program)
File "/home/aalaagib/data/clevr-iep/iep/models/module_net.py", line 171, in _forward_modules_json
f_str = programs.function_to_str(f)
File "/home/aalaagib/data/clevr-iep/iep/programs.py", line 129, in function_to_str
if f['value_inputs']:
IndexError: too many indices for tensor of dimension 1

I have print f which is
tensor([28, 4, 21, 35, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], device='cuda:0')
I hope if someone can help me
Thanks.

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@jcjohnson Thanks alot. you are right the problem in the pytorch version.
I have reshape the program first and made this change
elif type(program) is torch.Tensor and program.dim() <= 2:
final_module_outputs = self._forward_modules_ints(feats, program)

So it works now. Thanks again.

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我解决了这个问题，现在他可以很好的run在torch1.7 以及cuda 11以上版本了
I solved this problem, now he can run well on torch1.7 and cuda 11 and above
https://github.com/luyishisi/clevr-iep

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