def get_baseline_model()

in scripts/train_model.py [0:0]

• 59 lines of code
• 8 McCabe index (conditional complexity)

def get_baseline_model(args):
  vocab = utils.load_vocab(args.vocab_json)
  if args.baseline_start_from is not None:
    model, kwargs = utils.load_baseline(args.baseline_start_from)
  elif args.model_type == 'LSTM':
    kwargs = {
      'vocab': vocab,
      'rnn_wordvec_dim': args.rnn_wordvec_dim,
      'rnn_dim': args.rnn_hidden_dim,
      'rnn_num_layers': args.rnn_num_layers,
      'rnn_dropout': args.rnn_dropout,
      'fc_dims': parse_int_list(args.classifier_fc_dims),
      'fc_use_batchnorm': args.classifier_batchnorm == 1,
      'fc_dropout': args.classifier_dropout,
    }
    model = LstmModel(**kwargs)
  elif args.model_type == 'CNN+LSTM':
    kwargs = {
      'vocab': vocab,
      'rnn_wordvec_dim': args.rnn_wordvec_dim,
      'rnn_dim': args.rnn_hidden_dim,
      'rnn_num_layers': args.rnn_num_layers,
      'rnn_dropout': args.rnn_dropout,
      'cnn_feat_dim': parse_int_list(args.feature_dim),
      'cnn_num_res_blocks': args.cnn_num_res_blocks,
      'cnn_res_block_dim': args.cnn_res_block_dim,
      'cnn_proj_dim': args.cnn_proj_dim,
      'cnn_pooling': args.cnn_pooling,
      'fc_dims': parse_int_list(args.classifier_fc_dims),
      'fc_use_batchnorm': args.classifier_batchnorm == 1,
      'fc_dropout': args.classifier_dropout,
    }
    model = CnnLstmModel(**kwargs)
  elif args.model_type == 'CNN+LSTM+SA':
    kwargs = {
      'vocab': vocab,
      'rnn_wordvec_dim': args.rnn_wordvec_dim,
      'rnn_dim': args.rnn_hidden_dim,
      'rnn_num_layers': args.rnn_num_layers,
      'rnn_dropout': args.rnn_dropout,
      'cnn_feat_dim': parse_int_list(args.feature_dim),
      'stacked_attn_dim': args.stacked_attn_dim,
      'num_stacked_attn': args.num_stacked_attn,
      'fc_dims': parse_int_list(args.classifier_fc_dims),
      'fc_use_batchnorm': args.classifier_batchnorm == 1,
      'fc_dropout': args.classifier_dropout,
    }
    model = CnnLstmSaModel(**kwargs)
  if model.rnn.token_to_idx != vocab['question_token_to_idx']:
    # Make sure new vocab is superset of old
    for k, v in model.rnn.token_to_idx.items():
      assert k in vocab['question_token_to_idx']
      assert vocab['question_token_to_idx'][k] == v
    for token, idx in vocab['question_token_to_idx'].items():
      model.rnn.token_to_idx[token] = idx
    kwargs['vocab'] = vocab
    model.rnn.expand_vocab(vocab['question_token_to_idx'])
  model.cuda()
  model.train()
  return model, kwargs