Remove max-margin loss

It doesn't work

decanlp/arguments.py

@@ -126,7 +126,6 @@ def parse(argv):
 
     parser.add_argument('--skip_cache', action='store_true', dest='skip_cache_bool', help='whether to use exisiting cached splits or generate new ones')
     parser.add_argument('--lr_rate', default=0.001, type=float, help='initial_learning_rate')
-    parser.add_argument('--use_maxmargin_loss', action='store_true', help='whether to use max-margin loss or not')
     parser.add_argument('--small_glove', action='store_true', help='Use glove.6B.50d instead of glove.840B.300d')
     parser.add_argument('--almond_type_embeddings', action='store_true', help='Add type-based word embeddings for Almond task')
     parser.add_argument('--use_curriculum', action='store_true', help='Use curriculum learning')

decanlp/models/common.py

@@ -72,25 +72,6 @@ class LSTMDecoder(nn.Module):
 
         return input, (h_1, c_1)
 
-def max_margin_loss(probs, targets, pad_idx=1):
-
-    batch_size, max_length, depth = probs.size()
-    targets_mask = (targets != pad_idx).float()
-    flat_mask = targets_mask.view(batch_size*max_length,)
-    flat_preds = probs.view(batch_size*max_length, depth)
-
-    one_hot = torch.zeros_like(probs)
-    one_hot_gold = one_hot.scatter_(2, targets.unsqueeze(2), 1)
-
-    marginal_scores = probs - one_hot_gold + 1
-    marginal_scores = marginal_scores.view(batch_size*max_length, depth)
-    max_margin = torch.max(marginal_scores, dim=1)[0]
-
-    gold_score = torch.masked_select(flat_preds, one_hot_gold.view(batch_size*max_length, depth).byte())
-    margin = max_margin - gold_score
-
-    return torch.sum(margin*flat_mask) + 1e-8
-
 
 def positional_encodings_like(x, t=None):
     if t is None:

decanlp/models/multitask_question_answering_network.py

@@ -192,13 +192,8 @@ class MultitaskQuestionAnsweringNetwork(nn.Module):
                 decoder_vocab)
 
 
-            if self.args.use_maxmargin_loss:
-                targets = answer_indices[:, 1:].contiguous()
-                loss = max_margin_loss(probs, targets, pad_idx=pad_idx)
-
-            else:
-                probs, targets = mask(answer_indices[:, 1:].contiguous(), probs.contiguous(), pad_idx=pad_idx)
-                loss = F.nll_loss(probs.log(), targets)
+            probs, targets = mask(answer_indices[:, 1:].contiguous(), probs.contiguous(), pad_idx=pad_idx)
+            loss = F.nll_loss(probs.log(), targets)
             return loss, None
 
         else:

decanlp/util.py

@@ -196,7 +196,7 @@ def load_config_json(args):
         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', 'lower', 'glove_and_char',
-                    'use_maxmargin_loss', 'small_glove', 'almond_type_embeddings', 'almond_grammar',
+                    'small_glove', 'almond_type_embeddings', 'almond_grammar',
                     'trainable_decoder_embedding', 'glove_decoder', 'pretrained_decoder_lm',
                     'retrain_encoder_embedding', 'question', 'locale', 'use_google_translate']
 
@@ -205,7 +205,7 @@ def load_config_json(args):
                 setattr(args, r, config[r])
             elif r == 'locale':
                 setattr(args, r, 'en')
-            elif r in ('use_maxmargin_loss', 'small_glove', 'almond_type_embbedings'):
+            elif r in ('small_glove', 'almond_type_embbedings'):
                 setattr(args, r, False)
             elif r in ('glove_decoder', 'glove_and_char'):
                 setattr(args, r, True)