updates and fixes

arguments.py

@@ -25,7 +25,7 @@ def parse():
     Returns the arguments from the command line.
     """
     parser = ArgumentParser()
-    parser.add_argument('--root', default='/decaNLP', type=str, help='root directory for data, results, embeddings, code, etc.')
+    parser.add_argument('--root', default='./decaNLP', type=str, help='root directory for data, results, embeddings, code, etc.')
     parser.add_argument('--data', default='.data/', type=str, help='where to load data from.')
     parser.add_argument('--save', default='results', type=str, help='where to save results.')
     parser.add_argument('--embeddings', default='.embeddings', type=str, help='where to save embeddings.')

models/multitask_question_answering_network.py

@@ -11,7 +11,7 @@ from util import get_trainable_params, set_seed
 from modules import expectedBLEU, expectedMultiBleu, matrixBLEU
 
 from cove import MTLSTM
-#from allennlp.modules.elmo import Elmo, batch_to_ids
+from allennlp.modules.elmo import Elmo, batch_to_ids
 
 from .common import positional_encodings_like, INF, EPSILON, TransformerEncoder, TransformerDecoder, PackedLSTM, LSTMDecoderAttention, LSTMDecoder, Embedding, Feedforward, mask, CoattentiveLayer
 
@@ -119,29 +119,6 @@ class MultitaskQuestionAnsweringNetwork(nn.Module):
         else:
             context_embedded, question_embedded = context_elmo, question_elmo 
 
-        elif self.args.elmo:
-            context_list = []
-            for b in range(context.size(0)):
-                #FIXME
-                lc = [self.field.decoder_itos[i] if i < self.args.max_generative_vocab else self.field.decoder_itos[0] for i in context[b, :]]
-                if lc[0] == self.field.decoder_itos[2]:
-                    lc[0] = '<S>'
-                if lc[-1] == self.field.decoder_itos[3]:
-                    lc[-1] = '</S>'
-                context_list.append(lc)
-            question_list = []
-            for b in range(question.size(0)):
-                lq = [self.field.decoder_itos[i] if i < self.args.max_generative_vocab else self.field.decoder_itos[0] for i in question[b, :]]
-                if lq[0] == self.field.decoder_itos[2]:
-                    lq[0] = '<S>'
-                if lq[-1] == self.field.decoder_itos[3]:
-                    lq[-1] = '</S>'
-                question_list.append(lq)
-
-            context_embedded = self.project_elmo(torch.cat([self.elmo(batch_to_ids(context_list).to(self.device))['elmo_representations'][0], context_embedded], -1).detach())
-            question_embedded = self.project_elmo(torch.cat([self.elmo(batch_to_ids(question_list).to(self.device))['elmo_representations'][0], question_embedded], -1).detach())
-
-
         context_encoded = self.bilstm_before_coattention(context_embedded, context_lengths)[0]
         question_encoded = self.bilstm_before_coattention(question_embedded, question_lengths)[0]
 

predict.py

@@ -15,7 +15,6 @@ import models
 from text.torchtext.data.utils import get_tokenizer
 
 
-
 def get_all_splits(args, new_vocab):
     splits = []
     for task in args.tasks:
@@ -171,11 +170,7 @@ def run(args, field, val_sets, model):
                             a = from_all_answers(batch.woz_id.data.cpu())
                         else:
                             if task == 'almond':
-                                setattr(field, 'use_revtok', False)
-                                setattr(field, 'tokenize', tokenizer)
-                                a = field.reverse_almond(batch.answer.data)
-                                setattr(field, 'use_revtok', True)
-                                setattr(field, 'tokenize', 'revtok')
+                                a = field.reverse(batch.answer.data, detokenize=lambda x: ' '.join(x))
                             else:
                                 a = field.reverse(batch.answer.data)
                         for aa in a:
@@ -322,7 +317,6 @@ if __name__ == '__main__':
 
     print(f'Loading from {args.best_checkpoint}')
 
-    # save_dict = torch.load(args.best_checkpoint)
     if torch.cuda.is_available():
         save_dict = torch.load(args.best_checkpoint)
     else:

train.py

@@ -84,7 +84,7 @@ def prepare_data(args, field, logger):
 
     for task, s in zip(args.train_tasks, train_sets):
         for ex in s.examples[:10]:
-            print('examples***:', ex.context)
+            print('examples***:', [token.strip() for token in ex.context])
 
     if args.load is None:
         logger.info(f'Getting pretrained word vectors')

util.py

@@ -64,10 +64,10 @@ def preprocess_examples(args, tasks, splits, field, logger=None, train=True):
         if logger is not None:
             logger.info('Tokenized examples:')
             for ex in s.examples[:10]:
-                logger.info('Context: ' + ' '.join(ex.context))
-                logger.info('Question: ' + ' '.join(ex.question))
-                logger.info(' '.join(ex.context_question))
-                logger.info('Answer: ' + ' '.join(ex.answer))
+                logger.info('Context: ' + ' '.join([token.strip() for token in ex.context]))
+                logger.info('Question: ' + ' '.join([token.strip() for token in ex.question]))
+                logger.info(' '.join([token.strip() for token in ex.context_question]))
+                logger.info('Answer: ' + ' '.join([token.strip() for token in ex.answer]))
 
 
 

validate.py

@@ -44,7 +44,7 @@ def all_reverse(tensor, world_size, task, field, clip, dim=0):
     # for distributed training, dev sets are padded with extra examples so that the
     # tensors are all of a predictable size for all_gather. This line removes those extra examples
     if task == 'almond':
-        return field.reverse(tensor, detokenizer=lambda x: ' '.join(x))[:clip]
+        return field.reverse(tensor, detokenize=lambda x: ' '.join(x))[:clip]
     else:
         return field.reverse(tensor)[:clip]