Merge commit '510b0e02d1fcf43a5281bebb147ca1bce5db45f1' into self_cri…

…tical_bottom_up

* commit '510b0e02d1fcf43a5281bebb147ca1bce5db45f1':
  Fix a typo in FCModel _sample
  Remove att2in dependency and fix typo
  Fix #26.
  fix bug when lang_stats not set to 1
  Only initialize cider_score at the first time.

misc/rewards.py

@@ -75,4 +75,4 @@ def get_self_critical_reward(model, fc_feats, att_feats, att_masks, data, gen_re
 
     rewards = np.repeat(scores[:, np.newaxis], gen_result.shape[1], 1)
 
-    return rewards
+    return rewards

models/FCModel.py

@@ -152,7 +152,7 @@ def _sample(self, fc_feats, att_feats, att_masks=None, opt={}):
         beam_size = opt.get('beam_size', 1)
         temperature = opt.get('temperature', 1.0)
         if beam_size > 1:
-            return self.sample_beam(fc_feats, att_feats, opt)
+            return self._sample_beam(fc_feats, att_feats, opt)
 
         batch_size = fc_feats.size(0)
         state = self.init_hidden(batch_size)

models/__init__.py

@@ -12,14 +12,13 @@
 from .ShowTellModel import ShowTellModel
 from .FCModel import FCModel
 from .OldModel import ShowAttendTellModel, AllImgModel
-from .Att2inModel import Att2inModel
+# from .Att2inModel import Att2inModel
 from .AttModel import *
 
 def setup(opt):
-
     if opt.caption_model == 'fc':
         model = FCModel(opt)
-    if opt.caption_model == 'show_tell':
+    elif opt.caption_model == 'show_tell':
         model = ShowTellModel(opt)
     # Att2in model in self-critical
     elif opt.caption_model == 'att2in':
@@ -54,4 +53,4 @@ def setup(opt):
         assert os.path.isfile(os.path.join(opt.start_from,"infos_"+opt.id+".pkl")),"infos.pkl file does not exist in path %s"%opt.start_from
         model.load_state_dict(torch.load(os.path.join(opt.start_from, 'model.pth')))
 
-    return model
+    return model

train.py

@@ -92,7 +92,7 @@ def train(opt):
                 opt.current_lr = opt.learning_rate * decay_factor
             else:
                 opt.current_lr = opt.learning_rate
-            utils.set_lr(optimizer, opt.current_lr) # set the decayed rate
+            utils.set_lr(optimizer, opt.current_lr)
             # Assign the scheduled sampling prob
             if epoch > opt.scheduled_sampling_start and opt.scheduled_sampling_start >= 0:
                 frac = (epoch - opt.scheduled_sampling_start) // opt.scheduled_sampling_increase_every
@@ -169,8 +169,9 @@ def train(opt):
 
             # Write validation result into summary
             add_summary_value(tb_summary_writer, 'validation loss', val_loss, iteration)
-            for k,v in lang_stats.items():
-                add_summary_value(tb_summary_writer, k, v, iteration)
+            if lang_stats is not None:
+                for k,v in lang_stats.items():
+                    add_summary_value(tb_summary_writer, k, v, iteration)
             val_result_history[iteration] = {'loss': val_loss, 'lang_stats': lang_stats, 'predictions': predictions}
 
             # Save model if is improving on validation result