Comments (7)
aconneau
commented on June 2, 2024
The error message you have comes from this:
import numpy as np
embeddings = [np.zeros((64, 4096)), np.zeros((64, 4096))]
embeddings = np.vstack(embeddings) # no error
embeddings = [np.zeros((64, 4096)), np.zeros((64, 4096)), np.zeros((64, 3412))]
embeddings = np.vstack(embeddings) # error
# -> ValueError: all the input array dimensions except for the concatenation axis must match exactlyFor some reasons, one of the element in "embeddings" is not of size (batch_size=128, emb_dim=4096). So there must be one or more element of size different than (128, 4096).
- Just before the error in line 209, could you print the shape of each element in embeddings?
for batch in embeddings:
print(batch.shape)to see if we can spot the element with the wrong size.
-
What is in "sentences"? Can you check that you don't have an empty sentence?
-
what is the length of "sentences" ?
-
Could you update pytorch to a more recent version and see if you still have the issue?
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briandw
commented on June 2, 2024
Thanks for the quick response.
I believe that I'm on the latest torch version of 0.1.12_1. Is there a later version?
sentences length is is 9815 and there are no 0 length sentences in the array.
This is the output from just before the line 209:
Nb words kept : 128201/130068 (98.56 %)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 64, 4096)
(1, 23, 4096)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-8-3d88dd6254e6> in <module>()
1 tmp = sentences[:128]
----> 2 model.encode(sentences, tokenize=False, verbose=True)
/home/brian/InferSent/encoder/models.py in encode(self, sentences, bsize, tokenize, verbose)
210 for batch in embeddings:
211 print(batch.shape)
--> 212 embeddings = np.vstack(embeddings)
213
214 # unsort
/home/brian/anaconda3/envs/py2/lib/python2.7/site-packages/numpy/core/shape_base.pyc in vstack(tup)
235
236 """
--> 237 return _nx.concatenate([atleast_2d(_m) for _m in tup], 0)
238
239 def hstack(tup):
ValueError: all the input array dimensions except for the concatenation axis must match exactly
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aconneau
commented on June 2, 2024
Oh ok I get it. Can you try to change the line in models.py here: https://github.com/facebookresearch/InferSent/blob/master/encoder/models.py#L67
emb = torch.max(sent_output, 0)[0]
into:
emb = torch.max(sent_output, 0)[0].squeeze(0)
and see if this works then?
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briandw
commented on June 2, 2024
That's working now. Thanks! I wonder why this didn't show up before?
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aconneau
commented on June 2, 2024
@briandw So this is an issue linked to the change of policy in pytorch functions such as max, mean, sum etc.
If you have a tensor of size (say) (23, 128, 4096). If you take the torch.max (or torch.mean ..) over the first dimension, then you get a tensor of size:
(128, 4096) for recent versions of pytorch
(1, 128, 4096) for old versions of pytorch
So it means your version of pytorch is too old. I will update the requirement part in the README, and add an exception in the models.py to handle this case.
Thanks
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aconneau
commented on June 2, 2024
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Pragtisood
commented on June 2, 2024
setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (9815,) + inhomogeneous part.
getting this error on
embeddings = infersent.encode(sentences, bsize=128, tokenize=False, verbose=True)
print('nb sentences encoded : {0}'.format(len(embeddings)))
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Related Issues (20)
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