agakshat / visualdialog-pytorch Goto Github PK

Thanks for sharig the codes. I got an error when training with the command "python main.py --num_abots 3 --num_qbots 1 --scratch --outf save/temp_dir". My environment is Python 3.6 with pytorch 0.4.0. Can you help me about that?

(community) lvxinyu@12315:~$ CUDA_VISIBLE_DEVICES=4 python /home/lvxinyu/code/visualdialog-pytorch/main.py --num_abots 3 --num_qbots 1 --scratch --outf /home/lvxinyu/code/visualdialog-pytorch/data/v09/save/temp_dir
DataLoader loading: train
Loading image feature from data/vdl_img_vgg.h5
train number of data: 82783
Loading txt from data/visdial_data.h5
Vocab Size: 8964
DataLoader loading: test
Loading image feature from data/vdl_img_vgg.h5
test number of data: 40504
Loading txt from data/visdial_data.h5
Vocab Size: 8964
Initializing A-Bot and Q-Bot...
/home/lvxinyu/.local/lib/python3.6/site-packages/torch/nn/modules/rnn.py:38: UserWarning: dropout option adds dropout after all but last recurrent layer, so non-zero dropout expects num_layers greater than 1, but got dropout=0.5 and num_layers=1
"num_layers={}".format(dropout, num_layers))
Starting Epoch: 1 | K: 10
Done with Batch # 20 | Av. Time Per Batch: 1.090s
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Traceback (most recent call last):
File "/home/lvxinyu/code/visualdialog-pytorch/main.py", line 624, in
im_loss_epoch_n = train(epoch,k_curr)
File "/home/lvxinyu/code/visualdialog-pytorch/main.py", line 132, in train
lm_loss.backward()
File "/home/lvxinyu/.local/lib/python3.6/site-packages/torch/tensor.py", line 93, in backward
torch.autograd.backward(self, gradient, retain_graph, create_graph)
File "/home/lvxinyu/.local/lib/python3.6/site-packages/torch/autograd/init.py", line 89, in backward
allow_unreachable=True) # allow_unreachable flag
RuntimeError: The expanded size of the tensor (75) must match the existing size (58) at non-singleton dimension 0

Hi Akshat Agarwal,

Thanks for your sharing of this code. Recently I tried to run your code as an baseline, while I encontered some problems.

1. When I run the main.py file I got the following issue. Are you sure your code can be run successfully in the environment of Python 3.6 and Pytorch 0.4.0?

/home/wanyao/.conda/envs/py36/lib/python3.6/site-packages/torch/nn/modules/rnn.py:38: UserWarning: dropout option adds dropout after all but last recurrent layer, so non-zero dropout expects num_layers greater than 1, but got dropout=0.5 and num_layers=1
  "num_layers={}".format(dropout, num_layers))
Traceback (most recent call last):
  File "main.py", line 651, in <module>
    optimizerAbotRLarr = []
  File "main.py", line 115, in train

  File "/home/wanyao/.conda/envs/py36/lib/python3.6/site-packages/torch/nn/modules/module.py", line 491, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/wanyao/www/Dropbox/ghproj-py36/visualdialog-pytorch-original/networks/encoder_QIH.py", line 33, in forward
    img_emb = F.tanh(self.img_embed(img_raw.view(-1,1,self.img_feat_size)))
RuntimeError: invalid argument 2: size '[-1 x 1 x 4096]' is invalid for input with 7526400 elements at /pytorch/aten/src/TH/THStorage.c:37

2. My second concern is that what's the v09 folder used for? I know in the repos of visDial.pytorch from Jiasen, only the vdl_img_vgg.h5 is used for extract CNN features for images, why did you use both of the vdl_img_vgg.h5 and data_img_vgg16_pool5.h5 to extract the feature of images? Furthermore, since the file data_img_vgg16_pool5.h5 is too large (50gb), it will take lots of time to load it into memory, and I think it's time consuming to use this file for development/debug. Do you have any suggestion on accelerating this process, or can I just use the vdl_img_vgg.h5 to represent the images?

Hi Akshat,

Sorry to trouble you again. Recently I am reading your code and I have a naive question. For each bot, I am wondering why did you invoke the encoder for one time and the decoder for two times.

featG, ques_hidden1 = abots[abot_idx][0](ques_emb_g, his_emb_g, img_input, ques_hidden1, hist_hidden1, rnd+1)

_, ques_hidden1 = abots[abot_idx][2](featG.view(1, -1, opt.ninp), ques_hidden1)

logprob, _ = abots[abot_idx][2](ans_emb, ques_hidden1)

Usually, when I implement the encoder-decoder framework, I just encode the input into a hidden state and then decode it into sequence for only one time. I don't understand the second line of the above 3 lines of codes. Could you tell me what's the second line of code used for?

Thanks.

Hi, thanks for your sharing.
But, I'm a little confused. Do you evaluate with only 20 candidate answers?