fix bugs

scripts/convert_t2t_to_fairseq.py

@@ -354,14 +354,11 @@ def load_param():
 
 
   if share_all_embedding:
-    softmax_w_tensor = _get_tensor('symbol_modality_{}_{}/shared/weights'.format(tgt_vocab_size, emb_size),
-                                   shard=shard)
-  if share_decoder_input_and_softmax:
-    softmax_w_tensor = _get_tensor('symbol_modality_{}_{}/shared_target/weights'.format(tgt_vocab_size, emb_size),
-                                   shard=shard)
+    softmax_w_tensor = _get_tensor('symbol_modality_{}_{}/shared/weights'.format(tgt_vocab_size, emb_size),shard=shard)
+  elif share_decoder_input_and_softmax:
+    softmax_w_tensor = _get_tensor('symbol_modality_{}_{}/shared_target/weights'.format(tgt_vocab_size, emb_size),shard=shard)
   else:
-    softmax_w_tensor = _get_tensor('symbol_modality_{}_{}/softmax/weights'.format(tgt_vocab_size, emb_size),
-                                 shard=shard)
+    softmax_w_tensor = _get_tensor('symbol_modality_{}_{}/softmax/weights'.format(tgt_vocab_size, emb_size),shard=shard)
   # note: we transpose the matrix, from (V,H) to (H,V)
   param_dict['softmax_w'] = softmax_w_tensor
   return param_dict