somethings goes wrong after loading data

[tanyuqian]

Hi,

I encountered a problem when using your code. here is the command I used:

python script_bin/train_model.py \
	--trainer	--train-inputs cnn-dailymail/inputs/train \
			--train-labels cnn-dailymail/labels/train \
			--valid-inputs cnn-dailymail/inputs/valid \
			--valid-labels cnn-dailymail/labels/valid \
			--valid-refs cnn-dailymail/human-abstracts/valid \
			--weighted \
			--gpu 0 \
			--seed 12345678 \
	--emb	--pretrained-embeddings glove/glove.6B.200d.txt \
	--enc cnn \
	--ext s2s --bidirectional

and here is the msg on the screen:

{'train_inputs': PosixPath('cnn-dailymail/inputs/train'), 'train_labels': PosixPath('cnn-dailymail/labels/train'), 'valid_inputs': PosixPath('cnn-dailymail/inputs/valid'), 'valid_labels': PosixPath('cnn-dailymail/labels/valid'), 'valid_refs': PosixPath('cnn-dailymail/human-abstracts/valid'), 'seed': 12345678, 'epochs': 50, 'batch_size': 32, 'gpu': 0, 'teacher_forcing': 25, 'sentence_limit': 50, 'weighted': True, 'loader_workers': 8, 'raml_samples': 25, 'raml_temp': 0.05, 'summary_length': 100, 'remove_stopwords': False, 'shuffle_sents': False, 'model': None, 'results': None}

{'embedding_size': 200, 'pretrained_embeddings': 'glove/glove.6B.200d.txt', 'top_k': None, 'at_least': 1, 'word_dropout': 0.0, 'embedding_dropout': 0.25, 'update_rule': 'fix-all', 'filter_pretrained': False}

{'dropout': 0.25, 'filter_windows': [1, 2, 3, 4, 5, 6], 'feature_maps': [25, 25, 50, 50, 50, 50], 'OPT': 'cnn'}

{'hidden_size': 300, 'bidirectional': True, 'rnn_dropout': 0.25, 'num_layers': 1, 'cell': 'gru', 'mlp_layers': [100], 'mlp_dropouts': [0.25], 'OPT': 's2s'}
Initializing vocabulary and embeddings.
INFO:root: Reading pretrained embeddings from glove/glove.6B.200d.txt
INFO:root: Read 400002 embeddings of size 200
INFO:root: EmbeddingContext(
  (embeddings): Embedding(400002, 200, padding_idx=0)
)
Loading training data.
Loading validation data.
INFO:root: Computing class weights...
287113/287113
INFO:root: Counts y=0: 8980574, y=1 1119138
INFO:root: Reweighting y=1 by 8.024545677119354

Placing model on device: 0
INFO:root: Model parameter initialization started.
INFO:root: EmbeddingContext initialization started.
INFO:root: Initializing with pretrained embeddings.
INFO:root: EmbeddingContext initialization finished.
INFO:root: CNNSentenceEncoder initialization started.
INFO:root: filters.0.weight (25,1,1,1,200): Xavier normal init.
INFO:root: filters.0.bias (25): constant (0) init.
INFO:root: filters.1.weight (25,1,1,2,200): Xavier normal init.
INFO:root: filters.1.bias (25): constant (0) init.
INFO:root: filters.2.weight (50,1,1,3,200): Xavier normal init.
INFO:root: filters.2.bias (50): constant (0) init.
INFO:root: filters.3.weight (50,1,1,4,200): Xavier normal init.
INFO:root: filters.3.bias (50): constant (0) init.
INFO:root: filters.4.weight (50,1,1,5,200): Xavier normal init.
INFO:root: filters.4.bias (50): constant (0) init.
INFO:root: filters.5.weight (50,1,1,6,200): Xavier normal init.
INFO:root: filters.5.bias (50): constant (0) init.
INFO:root: CNNSentenceEncoder initialization finished.
INFO:root: Seq2SeqSentenceExtractor initialization started.
INFO:root: decoder_start (250): random normal init.
INFO:root: encoder_rnn.weight_ih_l0 (900,250): Xavier normal init.
INFO:root: encoder_rnn.weight_hh_l0 (900,300): Xavier normal init.
INFO:root: encoder_rnn.bias_ih_l0 (900): constant (0) init.
INFO:root: encoder_rnn.bias_hh_l0 (900): constant (0) init.
INFO:root: encoder_rnn.weight_ih_l0_reverse (900,250): Xavier normal init.
INFO:root: encoder_rnn.weight_hh_l0_reverse (900,300): Xavier normal init.
INFO:root: encoder_rnn.bias_ih_l0_reverse (900): constant (0) init.
INFO:root: encoder_rnn.bias_hh_l0_reverse (900): constant (0) init.
INFO:root: decoder_rnn.weight_ih_l0 (900,250): Xavier normal init.
INFO:root: decoder_rnn.weight_hh_l0 (900,300): Xavier normal init.
INFO:root: decoder_rnn.bias_ih_l0 (900): constant (0) init.
INFO:root: decoder_rnn.bias_hh_l0 (900): constant (0) init.
INFO:root: decoder_rnn.weight_ih_l0_reverse (900,250): Xavier normal init.
INFO:root: decoder_rnn.weight_hh_l0_reverse (900,300): Xavier normal init.
INFO:root: decoder_rnn.bias_ih_l0_reverse (900): constant (0) init.
INFO:root: decoder_rnn.bias_hh_l0_reverse (900): constant (0) init.
INFO:root: mlp.0.weight (100,1200): Xavier normal init.
INFO:root: mlp.0.bias (100): constant (0) init.
INFO:root: mlp.3.weight (1,100): Xavier normal init.
INFO:root: mlp.3.bias (1): constant (0) init.
INFO:root: Seq2SeqSentenceExtractor initialization finished.
INFO:root: Model parameter initialization finished.

INFO:ignite.engine.engine.Engine:Engine run starting with max_epochs=50
ERROR:ignite.engine.engine.Engine:Current run is terminating due to exception: 'NoneType' object has no attribute 'data'
ERROR:ignite.engine.engine.Engine:Engine run is terminating due to exception: 'NoneType' object has no attribute 'data'
Traceback (most recent call last):
  File "script_bin/train_model.py", line 79, in <module>
    main()
  File "script_bin/train_model.py", line 76, in main
    results_path=args["trainer"]["results"])
  File "/home/bowen/packages/anaconda3/lib/python3.6/site-packages/nnsum-1.0-py3.6.egg/nnsum/trainer/labels_mle_trainer.py", line 164, in labels_mle_trainer
  File "/home/bowen/packages/anaconda3/lib/python3.6/site-packages/pytorch_ignite-0.1.2-py3.6.egg/ignite/engine/engine.py", line 326, in run
    self._handle_exception(e)
  File "/home/bowen/packages/anaconda3/lib/python3.6/site-packages/pytorch_ignite-0.1.2-py3.6.egg/ignite/engine/engine.py", line 291, in _handle_exception
    raise e
  File "/home/bowen/packages/anaconda3/lib/python3.6/site-packages/pytorch_ignite-0.1.2-py3.6.egg/ignite/engine/engine.py", line 313, in run
    hours, mins, secs = self._run_once_on_dataset()
  File "/home/bowen/packages/anaconda3/lib/python3.6/site-packages/pytorch_ignite-0.1.2-py3.6.egg/ignite/engine/engine.py", line 280, in _run_once_on_dataset
    self._handle_exception(e)
  File "/home/bowen/packages/anaconda3/lib/python3.6/site-packages/pytorch_ignite-0.1.2-py3.6.egg/ignite/engine/engine.py", line 291, in _handle_exception
    raise e
  File "/home/bowen/packages/anaconda3/lib/python3.6/site-packages/pytorch_ignite-0.1.2-py3.6.egg/ignite/engine/engine.py", line 272, in _run_once_on_dataset
    self.state.output = self._process_function(self, batch)
  File "/home/bowen/packages/anaconda3/lib/python3.6/site-packages/nnsum-1.0-py3.6.egg/nnsum/trainer/labels_mle_trainer.py", line 188, in _update
AttributeError: 'NoneType' object has no attribute 'data'

I don't know what is the problem? Hope you can help me:)

Thanks!

[avineshpvs]

Since the very recent version pytorch, the gradient buffers are initialized only when needed. I could find a workaround by adding a check before clipping the gradients in nnsum/trainer/labels_mle_trainer.py

if hasattr(param.grad, 'data'):

https://github.com/kedz/nnsum/blob/4660dc240bca1ea7da30e563236ae455dc20b837/nnsum/trainer/labels_mle_trainer.py#L187-L188

[tlifcen]

Hello, I'd like to test the cnn-dailymail dataset, but I can not download the dataset. So, I used reddit dataset to test the code. I tried with the command:

python script_bin/train_model.py \
    --trainer --train-inputs  ../dataset/summarization/nnsum/reddit/reddit/inputs/train\
              --train-labels ../dataset/summarization/nnsum/reddit/reddit/abs_lables/train \
              --valid-inputs ../dataset/summarization/nnsum/reddit/reddit/inputs/valid \
              --valid-labels ../dataset/summarization/nnsum/reddit/reddit/abs_lables/valid \
              --valid-refs ../dataset/summarization/nnsum/reddit/reddit/human-abstracts/valid \
              --weighted \
              --gpu 0 \
              --model ../dataset/summarization/nnsum/reddit/model \
              --results ../dataset/summarization/nnsum/reddit/val_score \
              --seed 12345678 \
    --emb --pretrained-embeddings ../dataset/embedding/eng_word_embedding.word2vec.vec \
    --enc cnn \
    --ext s2s --bidirectional

However, some reference problems occurred:

{'train_inputs': PosixPath('../dataset/summarization/nnsum/reddit/reddit/inputs/train'), 'train_labels': PosixPath('../dataset/summarization/nnsum/reddit/reddit/abs_lables/train'), 'valid_inputs': PosixPath('../dataset/summarization/nnsum/reddit/reddit/inputs/valid'), 'valid_labels': PosixPath('../dataset/summarization/nnsum/reddit/reddit/abs_lables/valid'), 'valid_refs': PosixPath('../dataset/summarization/nnsum/reddit/reddit/human-abstracts/valid'), 'seed': 12345678, 'epochs': 50, 'batch_size': 32, 'gpu': 0, 'teacher_forcing': 25, 'sentence_limit': 50, 'weighted': True, 'loader_workers': 8, 'raml_samples': 25, 'raml_temp': 0.05, 'summary_length': 100, 'remove_stopwords': False, 'shuffle_sents': False, 'model': PosixPath('../dataset/summarization/nnsum/reddit/model'), 'results': PosixPath('../dataset/summarization/nnsum/reddit/val_score')}

{'embedding_size': 200, 'pretrained_embeddings': '../dataset/embedding/eng_word_embedding.word2vec.vec', 'top_k': None, 'at_least': 1, 'word_dropout': 0.0, 'embedding_dropout': 0.25, 'update_rule': 'fix-all', 'filter_pretrained': False}

{'dropout': 0.25, 'filter_windows': [1, 2, 3, 4, 5, 6], 'feature_maps': [25, 25, 50, 50, 50, 50], 'OPT': 'cnn'}

{'hidden_size': 300, 'bidirectional': True, 'rnn_dropout': 0.25, 'num_layers': 1, 'cell': 'gru', 'mlp_layers': [100], 'mlp_dropouts': [0.25], 'OPT': 's2s'}
Initializing vocabulary and embeddings.
INFO:root: Reading pretrained embeddings from ../dataset/embedding/eng_word_embedding.word2vec.vec
INFO:root: Read 559185 embeddings of size 200
INFO:root: EmbeddingContext(
  (embeddings): Embedding(559185, 200, padding_idx=0)
)
Loading training data.
Loading validation data.
Traceback (most recent call last):
  File "script_bin/train_model.py", line 79, in <module>
    main()
  File "script_bin/train_model.py", line 48, in main
    sentence_limit=args["trainer"]["sentence_limit"])
  File "/home/constant/anaconda3/lib/python3.7/site-packages/nnsum-1.0-py3.7.egg/nnsum/data/summarization_dataset.py", line 30, in __init__
  File "/home/constant/anaconda3/lib/python3.7/site-packages/nnsum-1.0-py3.7.egg/nnsum/data/summarization_dataset.py", line 59, in _collect_references
Exception: No references found for example id: 12he9h.32

I looked the data files, there are 12he9h.32.a.txt and 12he9h.32.d.txt in the path reddit/human-abstracts/valid. I do not know where the problem is.

Thanks for any help! @kedz

[alexookah]

@tlifcen i encountered the same problem. when you run python script_bin/train_model.py make sure you are using the correct paths.

assume your current directory is from python_script directory.