FileNotFoundError: [Errno 2] No such file or directory Error on Ubuntu when running 'bash ./scripts/atis/train.sh 0'

[AviVarma]

I am trying to run this on Ubuntu using the Anaconda enviroment provided. I successfully ran the bash ./pull_data.sh and was able to create the anaconda enviroment.

Note:

There were some adjustment required howerver, for example I had to downgrade astor to 0.7.1 (pip install astor==0.7.1) and had to manually install xgboost using conda install -c conda-forge xgboost or pip install xgboost.

My Input:

bash ./scripts/atis/train.sh 0

Console Ouptut:

Decoding: 100%|██████████| 491/491 [00:34<00:00, 14.04it/s]
Traceback (most recent call last):
  File "/home/avi/Documents/tranX/exp.py", line 570, in <module>
    train(args)
  File "/home/avi/Documents/tranX/exp.py", line 150, in train
    eval_results = evaluation.evaluate(dev_set.examples, model, evaluator, args,
  File "/home/avi/Documents/tranX/evaluation.py", line 61, in evaluate
    eval_result = evaluator.evaluate_dataset(examples, decode_results, fast_mode=eval_top_pred_only, args=args)
TypeError: evaluate_dataset() got an unexpected keyword argument 'args'
Namespace(seed=0, cuda=False, lang='python', asdl_file=None, mode='test', parser='default_parser', transition_system='python2', evaluator='default_evaluator', lstm='lstm', embed_size=128, action_embed_size=128, field_embed_size=64, type_embed_size=64, hidden_size=256, ptrnet_hidden_dim=32, att_vec_size=256, no_query_vec_to_action_map=False, readout='identity', query_vec_to_action_diff_map=False, sup_attention=False, no_parent_production_embed=False, no_parent_field_embed=False, no_parent_field_type_embed=False, no_parent_state=False, no_input_feed=False, no_copy=False, column_att='affine', answer_prune=True, vocab=None, glove_embed_path=None, train_file=None, dev_file=None, pretrain=None, batch_size=10, dropout=0.0, word_dropout=0.0, decoder_word_dropout=0.3, primitive_token_label_smoothing=0.0, src_token_label_smoothing=0.0, negative_sample_type='best', valid_metric='acc', valid_every_epoch=1, log_every=10, save_to='model', save_all_models=False, patience=5, max_num_trial=10, uniform_init=None, glorot_init=False, clip_grad=5.0, max_epoch=-1, optimizer='Adam', lr=0.001, lr_decay=0.5, lr_decay_after_epoch=0, decay_lr_every_epoch=False, reset_optimizer=False, verbose=False, eval_top_pred_only=False, load_model='saved_models/atis/model.atis.sup.lstm.hidden256.embed128.action128.field32.type32.dropout0.3.lr_decay0.5.beam5.vocab.freq2.bin.train.bin.glorot.with_par_info.no_copy.ls0.1.seed0.bin', beam_size=5, decode_max_time_step=110, sample_size=5, test_file='data/atis/test.bin', save_decode_to='decodes/atis/model.atis.sup.lstm.hidden256.embed128.action128.field32.type32.dropout0.3.lr_decay0.5.beam5.vocab.freq2.bin.train.bin.glorot.with_par_info.no_copy.ls0.1.seed0.bin.test.decode', features=None, load_reconstruction_model=None, load_paraphrase_model=None, load_reranker=None, tie_embed=False, train_decode_file=None, test_decode_file=None, dev_decode_file=None, metric='accuracy', num_workers=1, load_decode_results=None, unsup_loss_weight=1.0, unlabeled_file=None, example_preprocessor=None, sql_db_file=None)
load model from [saved_models/atis/model.atis.sup.lstm.hidden256.embed128.action128.field32.type32.dropout0.3.lr_decay0.5.beam5.vocab.freq2.bin.train.bin.glorot.with_par_info.no_copy.ls0.1.seed0.bin]
Traceback (most recent call last):
  File "/home/avi/Documents/tranX/exp.py", line 576, in <module>
    test(args)
  File "/home/avi/Documents/tranX/exp.py", line 464, in test
    params = torch.load(args.load_model, map_location=lambda storage, loc: storage)
  File "/home/avi/anaconda3/envs/tranx/lib/python3.9/site-packages/torch/serialization.py", line 594, in load
    with _open_file_like(f, 'rb') as opened_file:
  File "/home/avi/anaconda3/envs/tranx/lib/python3.9/site-packages/torch/serialization.py", line 230, in _open_file_like
    return _open_file(name_or_buffer, mode)
  File "/home/avi/anaconda3/envs/tranx/lib/python3.9/site-packages/torch/serialization.py", line 211, in __init__
    super(_open_file, self).__init__(open(name, mode))
FileNotFoundError: [Errno 2] No such file or directory: 'saved_models/atis/model.atis.sup.lstm.hidden256.embed128.action128.field32.type32.dropout0.3.lr_decay0.5.beam5.vocab.freq2.bin.train.bin.glorot.with_par_info.no_copy.ls0.1.seed0.bin'

[danshaub]

After some digging, I found that there are a few definitions of evaluate_dataset that have different parameters. I was able to get past that error by simply adding , args=None to every function header for evaluate_dataset that didn't already have one. I'll make a pull request so you can see the changes I made directly.