size mismatch

[memoer6]

Hi

Thanks very much for sharing the library. It's amazing

I think there is a size mismatch between the input features and the pre-trained weights posted on this site for the input layer

The pre-trained weights, stored in neuralcoref/weights folder of this repository, have a size of ( 1000 x 668 ) for single mentions, and ( 1000 x 1364 ) for pair mentions in layer 0

However, the dimension of input features generated for each mention is 674 per single mention, and 1370 per pair mentions, if compressed = False.

This mismatch generates an error during the evaluation process if the pre-trained weights are loaded, as shown below

Do you have the same problem?

Thanks,

`---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
in ()
61 eval_evaluator.test_model()
62 start_time = time.time()
---> 63 eval_evaluator.build_test_file()
64 score, f1_conll, ident = eval_evaluator.get_score()
65 elapsed = time.time() - start_time

~/coding/coreference/notebook/learning/evaluator.py in build_test_file(self, out_path, remove_singleton, print_all_mentions, debug)
162 cur_m = 0
163 for sample_batched, mentions_idx, n_pairs_l in zip(self.dataloader, self.mentions_idx, self.n_pairs):
--> 164 scores, max_i = self.get_max_score(sample_batched)
165 for m_idx, ind, n_pairs in zip(mentions_idx, max_i, n_pairs_l):
166 if ind < n_pairs : # the single score is not the highest, we have a match !

~/coding/coreference/notebook/learning/evaluator.py in get_max_score(self, batch, debug)
140 mask = mask.cuda()
141 self.model.eval()
--> 142 scores = self.model.forward(inputs, concat_axis=1).data
143 scores.masked_fill_(mask, -float('Inf'))
144 _, max_idx = scores.max(dim=1) # We may want to weight the single score with coref.greedyness

~/coding/coreference/notebook/learning/model.py in forward(self, inputs, concat_axis)
72 embed_words = self.drop(self.word_embeds(words).view(words.size()[0], -1))
73 single_input = torch.cat([spans, embed_words, single_features], 1)
---> 74 single_scores = self.single_top(single_input)
75 if pairs:
76 batchsize, pairs_num, _ = ana_spans.size()

~/software/anaconda3/envs/py36/lib/python3.6/site-packages/torch/nn/modules/module.py in call(self, *input, **kwargs)
355 result = self._slow_forward(*input, **kwargs)
356 else:
--> 357 result = self.forward(*input, **kwargs)
358 for hook in self._forward_hooks.values():
359 hook_result = hook(self, input, result)

~/software/anaconda3/envs/py36/lib/python3.6/site-packages/torch/nn/modules/container.py in forward(self, input)
65 def forward(self, input):
66 for module in self._modules.values():
---> 67 input = module(input)
68 return input
69

~/software/anaconda3/envs/py36/lib/python3.6/site-packages/torch/nn/modules/module.py in call(self, *input, **kwargs)
355 result = self._slow_forward(*input, **kwargs)
356 else:
--> 357 result = self.forward(*input, **kwargs)
358 for hook in self._forward_hooks.values():
359 hook_result = hook(self, input, result)

~/software/anaconda3/envs/py36/lib/python3.6/site-packages/torch/nn/modules/linear.py in forward(self, input)
53
54 def forward(self, input):
---> 55 return F.linear(input, self.weight, self.bias)
56
57 def repr(self):

~/software/anaconda3/envs/py36/lib/python3.6/site-packages/torch/nn/functional.py in linear(input, weight, bias)
833 if input.dim() == 2 and bias is not None:
834 # fused op is marginally faster
--> 835 return torch.addmm(bias, input, weight.t())
836
837 output = input.matmul(weight.t())

RuntimeError: size mismatch, m1: [1 x 674], m2: [668 x 1000] at /opt/conda/conda-bld/pytorch_1523244252089/work/torch/lib/TH/generic/THTensorMath.c:1434 `

[stale]

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