touching up predict

predict.py

@@ -64,6 +64,8 @@ def to_iter(data, bs, device):
 def run(args, field, val_sets, model):
     device = set_seed(args)
     print(f'Preparing iterators')
+    if len(args.val_batch_size) == 1 and len(val_sets) > 1:
+        args.val_batch_size *= len(val_sets)
     iters = [(name, to_iter(x, bs, device)) for name, x, bs in zip(args.tasks, val_sets, args.val_batch_size)]
  
     def mult(ps):
@@ -82,10 +84,11 @@ def run(args, field, val_sets, model):
     model.eval()
     with torch.no_grad():
         for task, it in iters:
+            print(task)
             prediction_file_name = os.path.join(os.path.splitext(args.best_checkpoint)[0], args.evaluate, task + '.txt')
             answer_file_name = os.path.join(os.path.splitext(args.best_checkpoint)[0], args.evaluate, task + '.gold.txt')
             results_file_name = answer_file_name.replace('gold', 'results')
-            if 'sql' in task:
+            if 'sql' in task or 'squad' in task:
                 ids_file_name = answer_file_name.replace('gold', 'ids')
             if os.path.exists(prediction_file_name):
                 print('** ', prediction_file_name, ' already exists -- this is where predictions are stored **')
@@ -104,14 +107,15 @@ def run(args, field, val_sets, model):
             if not os.path.exists(prediction_file_name) or args.overwrite_predictions:
                 with open(prediction_file_name, 'w') as prediction_file:
                     predictions = []
-                    wikisql_ids = []
+                    ids = []
                     for batch_idx, batch in enumerate(it):
                         _, p = model(batch)
                         p = field.reverse(p)
                         for i, pp in enumerate(p):
                             if 'sql' in task:
-                                wikisql_id = int(batch.wikisql_id[i])
-                                wikisql_ids.append(wikisql_id)
+                                ids.append(int(batch.wikisql_id[i]))
+                            if 'squad' in task:
+                                ids.append(it.dataset.q_ids[int(batch.squad_id[i])])
                             prediction_file.write(pp + '\n')
                             predictions.append(pp) 
             else:
@@ -120,9 +124,14 @@ def run(args, field, val_sets, model):
     
             if 'sql' in task:
                 with open(ids_file_name, 'w') as id_file:
-                    for i in wikisql_ids:
+                    for i in ids:
                         id_file.write(json.dumps(i) + '\n')
-    
+
+            if 'squad' in task:
+                with open(ids_file_name, 'w') as id_file:
+                    for i in ids:
+                        id_file.write(i + '\n')
+   
             def from_all_answers(an):
                 return [it.dataset.all_answers[sid] for sid in an.tolist()] 
     
@@ -165,7 +174,7 @@ def get_args():
     parser = ArgumentParser()
     parser.add_argument('--path', required=True)
     parser.add_argument('--evaluate', type=str, required=True)
-    parser.add_argument('--tasks', default=['wikisql', 'woz.en', 'cnn_dailymail', 'iwslt.en.de', 'zre', 'srl', 'squad', 'sst', 'multinli.in.out', 'schema'], nargs='+')
+    parser.add_argument('--tasks', default=['squad', 'iwslt.en.de', 'cnn_dailymail', 'multinli.in.out', 'sst', 'srl', 'zre', 'woz.en', 'wikisql', 'schema'], nargs='+')
     parser.add_argument('--gpus', default=[0], nargs='+', type=int, help='a list of gpus that can be used (multi-gpu currently WIP)')
     parser.add_argument('--seed', default=123, type=int, help='Random seed.')
     parser.add_argument('--data', default='/decaNLP/.data/', type=str, help='where to load data from.')
@@ -180,7 +189,7 @@ def get_args():
 
     with open(os.path.join(args.path, 'config.json')) as config_file:
         config = json.load(config_file)
-        retrieve = ['model', 'val_batch_size',
+        retrieve = ['model', 
                     'transformer_layers', 'rnn_layers', 'transformer_hidden', 
                     'dimension', 'load', 'max_val_context_length', 'val_batch_size', 
                     'transformer_heads', 'max_output_length', 'max_generative_vocab', 

text/torchtext/datasets/generic.py

@@ -211,10 +211,10 @@ class SQuAD(CQA, data.Dataset):
         fields = [(x, field) for x in self.fields]
         cache_name = os.path.join(os.path.dirname(path), '.cache', os.path.basename(path), str(subsample))
 
-        examples, all_answers = [], []
+        examples, all_answers, q_ids = [], [], []
         if os.path.exists(cache_name):
             print(f'Loading cached data from {cache_name}')
-            examples, all_answers = torch.load(cache_name)
+            examples, all_answers, q_ids = torch.load(cache_name)
         else:
             with open(os.path.expanduser(path)) as f:
                 squad = json.load(f)['data']
@@ -226,6 +226,7 @@ class SQuAD(CQA, data.Dataset):
                         qas = paragraph['qas']
                         for qa in qas:
                             question = ' '.join(qa['question'].split())
+                            q_ids.append(qa['id'])
                             squad_id = len(all_answers)
                             context_question = get_context_question(context, question) 
                             if len(qa['answers']) == 0:
@@ -303,7 +304,7 @@ class SQuAD(CQA, data.Dataset):
 
             os.makedirs(os.path.dirname(cache_name), exist_ok=True)
             print(f'Caching data to {cache_name}')
-            torch.save((examples, all_answers), cache_name)
+            torch.save((examples, all_answers, q_ids), cache_name)
 
 
         FIELD = data.Field(batch_first=True, use_vocab=False, sequential=False, 
@@ -315,6 +316,7 @@ class SQuAD(CQA, data.Dataset):
 
         super(SQuAD, self).__init__(examples, fields, **kwargs)
         self.all_answers = all_answers
+        self.q_ids = q_ids
 
 
     @classmethod