wanyao1992 / code_summarization_public Goto Github PK

我在运行时，报如下错误：
Traceback (most recent call last):
File "a2c-train.py", line 348, in
main()
File "a2c-train.py", line 330, in main
xent_trainer.train(opt.start_epoch, opt.start_reinforce - 1, start_time)
File "F:\code_summarization_public-master\lib\train\Trainer.py", line 30, in train
train_loss = self.train_epoch(epoch)
File "F:\code_summarization_public-master\lib\train\Trainer.py", line 103, in train_epoch
return total_loss / total_words
ZeroDivisionError: division by zero
failed.
您在实验中是否遇到类似的问题，希望您可以帮我解答一下，万分感谢！

### Run time Log:
python a2c-train.py -data dataset/train/processed_all.train.pt -save_dir dataset//result/ -embedding_w2v dataset/train/ -start_reinforce 10 -end_epoch 30 -critic_pretrain_epochs 10 -data_type hybrid -has_attn 1 -gpus 0
Start...

• vocabulary size. source = 50004; target = 31415
• number of XENT training sentences. 54426
• number of PG training sentences. 54426
• maximum batch size. 32
  Building model...
  use_critic: True
  /usr/local/lib/python3.6/dist-packages/torch/nn/modules/rnn.py:50: UserWarning: dropout option adds dropout after all but last recurrent layer, so non-zero dropout expects num_layers greater than 1, but got dropout=0.3 and num_layers=1
  "num_layers={}".format(dropout, num_layers))
  model: Hybrid2SeqModel(
  (code_encoder): TreeEncoder(
  (word_lut): Embedding(50004, 512, padding_idx=0)
  (leaf_module): BinaryTreeLeafModule(
  (cx): Linear(in_features=512, out_features=512, bias=True)
  (ox): Linear(in_features=512, out_features=512, bias=True)
  )
  (composer): BinaryTreeComposer(
  (ilh): Linear(in_features=512, out_features=512, bias=True)
  (irh): Linear(in_features=512, out_features=512, bias=True)
  (lflh): Linear(in_features=512, out_features=512, bias=True)
  (lfrh): Linear(in_features=512, out_features=512, bias=True)
  (rflh): Linear(in_features=512, out_features=512, bias=True)
  (rfrh): Linear(in_features=512, out_features=512, bias=True)
  (ulh): Linear(in_features=512, out_features=512, bias=True)
  (urh): Linear(in_features=512, out_features=512, bias=True)
  )
  )
  (text_encoder): Encoder(
  (word_lut): Embedding(50004, 512, padding_idx=0)
  (rnn): LSTM(512, 512, dropout=0.3)
  )
  (decoder): HybridDecoder(
  (word_lut): Embedding(31415, 512, padding_idx=0)
  (rnn): StackedLSTM(
  (dropout): Dropout(p=0.3, inplace=False)
  (layers): ModuleList(
  (0): LSTMCell(1024, 512)
  )
  )
  (attn): HybridAttention(
  (linear_in): Linear(in_features=512, out_features=512, bias=False)
  (sm): Softmax(dim=None)
  (linear_out): Linear(in_features=2048, out_features=512, bias=False)
  (tanh): Tanh()
  )
  (dropout): Dropout(p=0.3, inplace=False)
  )
  (generator): BaseGenerator(
  (generator): Linear(in_features=512, out_features=31415, bias=True)
  )
  )
  optim: <lib.train.Optim.Optim object at 0x7f34d70f0c50>
  opt.start_reinforce: 10
• number of parameters: 92592823
  opt.eval: False
  opt.eval_sample: False
  supervised_data.src: 54426
  supervised_data.tgt: 54426
  supervised_data.trees: 54426
  supervised_data.leafs: 54426
  supervised training..
  start_epoch: 1
• XENT epoch *
  Model optim lr: 0.001
  <class 'lib.data.Dataset.Dataset'> 54426
  /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1351: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.
  warnings.warn("nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.")
  /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1340: UserWarning: nn.functional.tanh is deprecated. Use torch.tanh instead.
  warnings.warn("nn.functional.tanh is deprecated. Use torch.tanh instead.")
  /content/drive/My Drive/notebooks/Python_method_name_prediction/code_summarization_public/lib/model/HybridAttention.py:34: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.
  attn_tree = self.sm(attn_tree)
  /content/drive/My Drive/notebooks/Python_method_name_prediction/code_summarization_public/lib/model/HybridAttention.py:36: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.
  attn_txt = self.sm(attn_txt)
  outputs: torch.Size([26, 32, 512])
  /content/drive/My Drive/notebooks/Python_method_name_prediction/code_summarization_public/lib/metric/Loss.py:8: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.
  log_dist = F.log_softmax(logits)
  loss value: 3042.23095703125
  ---else---
  torch.Size([26, 32, 512])
  torch.Size([26, 32, 512])
  Traceback (most recent call last):
  File "a2c-train.py", line 339, in
  main()
  File "a2c-train.py", line 321, in main
  xent_trainer.train(opt.start_epoch, opt.start_reinforce - 1, start_time)
  File "/content/drive/My Drive/notebooks/Python_method_name_prediction/code_summarization_public/lib/train/Trainer.py", line 30, in train
  train_loss = self.train_epoch(epoch)
  File "/content/drive/My Drive/notebooks/Python_method_name_prediction/code_summarization_public/lib/train/Trainer.py", line 85, in train_epoch
  loss = self.model.backward(outputs, targets, weights, num_words, self.loss_func)
  File "/content/drive/My Drive/notebooks/Python_method_name_prediction/code_summarization_public/lib/model/EncoderDecoder.py", line 547, in backward
  outputs.backward(grad_output)
  File "/usr/local/lib/python3.6/dist-packages/torch/tensor.py", line 195, in backward
  torch.autograd.backward(self, gradient, retain_graph, create_graph)
  File "/usr/local/lib/python3.6/dist-packages/torch/autograd/init.py", line 99, in backward
  allow_unreachable=True) # allow_unreachable flag
  RuntimeError: Function CatBackward returned an invalid gradient at index 1 - got [85, 1, 512] but expected shape compatible with [57, 1, 512]
  failed.

你好，我想单独download数据集，但是无法下载

您好，您论文中 We can see that the actor and critic networks share the modules (a)-(b)-(c), reducing the number of learning parameters a lot. 意思是它俩共享参数吗？如果是的话，我在代码中好像没有看到；如果不是的话，请问您这是什么意思？

Get this error on running the testing part (tried it on two separate systems to get the same error):

Start...

• vocabulary size. source = 50004; target = 31280
• number of XENT training sentences. 1000
• number of PG training sentences. 1000
• maximum batch size. 64
  Building model...
  ('use_critic: ', False)
  Loading from checkpoint at /media/BACKUP/ghproj_d/code_summarization/github-python/result/model_rf_hybrid_1_29_reinforce.pt
  Traceback (most recent call last):
  File "a2c-train.py", line 349, in
  main()
  File "a2c-train.py", line 254, in main
  checkpoint = torch.load(opt.load_from, map_location=lambda storage, loc: storage)
  File "/usr/local/lib/python2.7/dist-packages/torch/serialization.py", line 231, in load
  return _load(f, map_location, pickle_module)
  File "/usr/local/lib/python2.7/dist-packages/torch/serialization.py", line 369, in _load
  magic_number = pickle_module.load(f)
  cPickle.UnpicklingError: could not find MARK

Any help would be appreciated.

您好，您这篇文章很好，我正在研究代码。考虑到您也是**人，我就直接用中文提问了。
我在论文中看到您有一个mlp，但在代码里我在create_critic里好像没看到相关的操作，与actor的区别仅仅是gen_out_size=1。
我接触pytorch不久，或许是我遗漏了什么，希望您能指明mlp的相关操作在哪个文件哪个函数下面，非常感谢您的帮助。